| { | |
| "paper_id": "I11-1011", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:31:39.984724Z" | |
| }, | |
| "title": "Single and Multi-objective Optimization for Feature Selection in Anaphora Resolution", | |
| "authors": [ | |
| { | |
| "first": "Sriparna", | |
| "middle": [], | |
| "last": "Saha", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "IIT Patna", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "sriparna@iitp.ac.in" | |
| }, | |
| { | |
| "first": "Asif", | |
| "middle": [], | |
| "last": "Ekbal", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "IIT Patna", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Olga", | |
| "middle": [], | |
| "last": "Uryupina", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Trento", | |
| "location": {} | |
| }, | |
| "email": "uryupina@unitn.it" | |
| }, | |
| { | |
| "first": "Massimo", | |
| "middle": [], | |
| "last": "Poesio", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Trento", | |
| "location": {} | |
| }, | |
| "email": "poesio@essex.ac.uk" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "There is no generally accepted metric for measuring the performance of anaphora resolution systems, and the existing metrics-MUC, B 3 , CEAF, Blanc, among others-tend to reward significantly different behaviors. Systems optimized according to one metric tend to perform poorly with respect to other ones, making it very difficult to compare anaphora resolution systems, as clearly shown by the results of the SEMEVAL 2010 Multilingual Coreference task. One solution would be to find a single completely satisfactory metric, but it's not clear whether this is possible and at any rate it is not going to happen any time soon. An alternative is to optimize models according to multiple metrics simultaneously. In this paper, we show, first of all, that this is possible to develop such models using Multi-objective Optimization (MOO) techniques based on Genetic Algorithms. Secondly, we show that optimizing according to multiple metrics simultaneously may result in better results with respect to each individual metric than optimizing according to that metric only.", | |
| "pdf_parse": { | |
| "paper_id": "I11-1011", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "There is no generally accepted metric for measuring the performance of anaphora resolution systems, and the existing metrics-MUC, B 3 , CEAF, Blanc, among others-tend to reward significantly different behaviors. Systems optimized according to one metric tend to perform poorly with respect to other ones, making it very difficult to compare anaphora resolution systems, as clearly shown by the results of the SEMEVAL 2010 Multilingual Coreference task. One solution would be to find a single completely satisfactory metric, but it's not clear whether this is possible and at any rate it is not going to happen any time soon. An alternative is to optimize models according to multiple metrics simultaneously. In this paper, we show, first of all, that this is possible to develop such models using Multi-objective Optimization (MOO) techniques based on Genetic Algorithms. Secondly, we show that optimizing according to multiple metrics simultaneously may result in better results with respect to each individual metric than optimizing according to that metric only.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "In anaphora resolution, 1 as in other HLT tasks, optimization to a metric is essential to achieve good performance (Hoste, 2005; Uryupina, 2010) . However, many evaluation metrics have been proposed for anaphora resolution, each capturing what seems to be a key intuition about the task: from MUC (Vilain et al., 1995) to B 3 (Bagga and Baldwin, 1998) , from the ACE metric (Doddington et al., 2004) to CEAF (Luo, 2005) to BLANC (Recasens and Hovy, 2011) . And unlike in other areas of HLT, none has really taken over. This would not matter so much if those metrics were to reward the same systems; but in fact, as dramatically demonstrated by the results of the Coreference Task at SEMEVAL 2010 (Recasens et al., 2010) , the opposite is true-almost every system could come on top depending on which metric was chosen.", | |
| "cite_spans": [ | |
| { | |
| "start": 115, | |
| "end": 128, | |
| "text": "(Hoste, 2005;", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 129, | |
| "end": 144, | |
| "text": "Uryupina, 2010)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 297, | |
| "end": 318, | |
| "text": "(Vilain et al., 1995)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 337, | |
| "end": 351, | |
| "text": "Baldwin, 1998)", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 374, | |
| "end": 399, | |
| "text": "(Doddington et al., 2004)", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 408, | |
| "end": 419, | |
| "text": "(Luo, 2005)", | |
| "ref_id": "BIBREF9" | |
| }, | |
| { | |
| "start": 429, | |
| "end": 454, | |
| "text": "(Recasens and Hovy, 2011)", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 696, | |
| "end": 719, | |
| "text": "(Recasens et al., 2010)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "It seems unlikely that the field will converge on a single metric any time soon. Given that many of the proposed metrics do capture what would seem to be plausible intuitions, it would seem desirable to develop methods to optimize systems according to more than one metric at once-in particular, according to at least one metric of what we might call the 'link-based' cluster of metrics (e.g., MUC) and at least one of what we will call the 'entity-based' cluster (e.g., CEAF).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "As it happens, techniques for doing just that have been developed in the area of Genetic Algorithms: so-called multi-objective optimization (MOO) (Deb, 2001) techniques. In this paper, we will show how these techniques can be used to optimize anaphora resolution models (we focused for the time being on feature selection) by looking for a solution in the space defined by a multiplicity of metrics (we used MUC and CEAF (in two variants) as the optimization functions). Perhaps the most interesting result of this work is the finding that by working in such a multi-metric space it is possible to find solutions that are better with respect to an individual metric than when trying to optimize for that metric alone-which arguably suggests that indeed both families of metrics capture some fundamental intuition about anaphora, and taking into account both intuitions we avoid local optima.", | |
| "cite_spans": [ | |
| { | |
| "start": 146, | |
| "end": 157, | |
| "text": "(Deb, 2001)", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "The structure of the paper is as follows. We first review the literature on using genetic algorithms for both single function and multi function opti-mization. Next, we discuss the particular method of multi-objective optimization we used in this paper, Non-Dominated Sorting Genetic Algorithm II (Deb et al., 2002) . After that we discuss how the method was used, and present our results. We then compare our work with other approaches to optimization for anaphora found in the literature.", | |
| "cite_spans": [ | |
| { | |
| "start": 297, | |
| "end": 315, | |
| "text": "(Deb et al., 2002)", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "A great number of statistical approaches to anaphora resolution have been proposed in the past ten years. These approaches differ with respect to their underlying models (e.g., mention pair model (Soon et al., 2001 ) vs. tournament model (Iida et al., 2003; Yang et al., 2005) , vs. entity-model (Luo et al., 2004) ), machine learners (e.g., decision trees vs. maximum entropy vs. SVMs vs. TiMBL) and their parameters, and with respect to feature sets used. There have been, however, only few attempts at explicit optimization of these aspects, and in those few cases, optimization tends to be done by hand. An early step in this direction was the work by Ng and Cardie (2002) , who developed a rich feature set including 53 features, but reported no significant improvement over their baseline when all these features were used with the MUC6 and MUC7 corpora. They then proceeded to manually select a subset of features that did yield better results for the MUC-6/7 datasets. A much larger scale and very systematic effort of manual feature selection over the same dataset was carried out by Uryupina (2007) , who evaluated over 600 features.", | |
| "cite_spans": [ | |
| { | |
| "start": 196, | |
| "end": 214, | |
| "text": "(Soon et al., 2001", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 238, | |
| "end": 257, | |
| "text": "(Iida et al., 2003;", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 258, | |
| "end": 276, | |
| "text": "Yang et al., 2005)", | |
| "ref_id": "BIBREF22" | |
| }, | |
| { | |
| "start": 296, | |
| "end": 314, | |
| "text": "(Luo et al., 2004)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 656, | |
| "end": 676, | |
| "text": "Ng and Cardie (2002)", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 1093, | |
| "end": 1108, | |
| "text": "Uryupina (2007)", | |
| "ref_id": "BIBREF18" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Background: Optimizing for Anaphora Resolution", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "The first systematic attempt at automatic optimization of anaphora resolution we are aware of was carried out by Hoste (2005) , who investigated the possibility of using genetic algorithms for automatic optimization of both feature selection and of learning parameters, also considering two different machine learners, TiMBL and Ripper. Her results suggest that such techniques yield improvements on the MUC-6/7 datasets. Recasens and Hovy (2009) carried out an investigation of feature selection for Spanish using the ANCORA corpus.", | |
| "cite_spans": [ | |
| { | |
| "start": 113, | |
| "end": 125, | |
| "text": "Hoste (2005)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 422, | |
| "end": 446, | |
| "text": "Recasens and Hovy (2009)", | |
| "ref_id": "BIBREF13" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Background: Optimizing for Anaphora Resolution", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "These approaches focused on a single metric only; the one proposal simultaneously to consider multiple metrics, Zhao and Ng (2010) still optimized for each metric individually.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Background: Optimizing for Anaphora Resolution", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "The effect of optimization on anaphora resolution was dramatically demonstrated by Uryupina's contribution to SEMEVAL 2010 Multilingual Coreference Task (Uryupina, 2010) . Uryupina directly optimizes two parameters of her system: the choice of a model (mention-pair vs. ILP with various constraints) and the definition of mention types for training separate classifiers. The optimization is done on the development data in a brute-force fashion, in order to maximize the performance according to a pre-defined metric (MUC, CEAF or BLANC). The results on the SEMEVAL-10 dataset clearly show that existing metrics of coreference rely on different intuitions and therefore a system, optimized for a particular metric, might show inferior results for the other ones. For example, the reported BLANC difference between the runs optimized for BLANC and CEAF is around 10 percentage points.", | |
| "cite_spans": [ | |
| { | |
| "start": 153, | |
| "end": 169, | |
| "text": "(Uryupina, 2010)", | |
| "ref_id": "BIBREF19" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Background: Optimizing for Anaphora Resolution", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "This highlights the importance of the multiobjective optimization (MOO) for coreference, that suggests a family of systems, showing reliable performance according to all the desired metrics. A form of MOO was applied to coreference by Munson et al. (2005) . Their general conclusion was negative, stating that \"ensemble selection seems too unreliable for use in NLP\", but they did see some improvements for coreference.", | |
| "cite_spans": [ | |
| { | |
| "start": 235, | |
| "end": 255, | |
| "text": "Munson et al. (2005)", | |
| "ref_id": "BIBREF10" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Background: Optimizing for Anaphora Resolution", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "In this section, we review optimization techniques using genetic algorithms (GAs) (Goldberg, 1989) . We first discuss single objective optimization, that can optimize according to a single objective function, and then multi-objective optimization (MOO), that can optimize more than one objective function, in particular, a popular MOO technique named Non-dominated Sorting Genetic Algorithm (NSGA)-II (Deb et al., 2002) .", | |
| "cite_spans": [ | |
| { | |
| "start": 82, | |
| "end": 98, | |
| "text": "(Goldberg, 1989)", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 401, | |
| "end": 419, | |
| "text": "(Deb et al., 2002)", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Optimization with Genetic Algorithms", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Genetic algorithms (GAs) (Goldberg, 1989) are randomized search and optimization techniques guided by the principles of evolution and natural genetics. In GAs the parameters of the search space are encoded in the form of strings (called chromosomes). A collection of such strings is called a population. Initially, a random population is created, which represents different points in the search space. An objective or fitness function is associated with each string that represents the degree of goodness of the string. Based on the principle of survival of the fittest, a few of the strings are selected and each is assigned a number of copies that go into the mating pool. Biologically inspired op-erators like crossover and mutation are applied on these strings to yield a new generation of strings. The processes of selection, crossover and mutation continues for a fixed number of generations or till a termination condition is satisfied.", | |
| "cite_spans": [ | |
| { | |
| "start": 25, | |
| "end": 41, | |
| "text": "(Goldberg, 1989)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Genetic Algorithms", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Multi-objective optimization (MOO) can be formally stated as follows (Deb, 2001 ). Find the vectors", | |
| "cite_spans": [ | |
| { | |
| "start": 69, | |
| "end": 79, | |
| "text": "(Deb, 2001", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "x * = [x * 1 , x * 2 , . . . , x * n ]", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "T of decision variables that simultaneously optimize the M objective values", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "{f 1 (x), f 2 (x), . . . , f M (x)}", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "while satisfying the constraints, if any.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "An important concept in MOO is that of domination. In the context of a maximization problem, a solution", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "x i is said to dominate x j if \u2200k \u2208 1, 2, . . . , M , f k (x i ) \u2265 f k (x j ) and \u2203k \u2208 1, 2, . . . , M , such that f k (x i ) > f k (x j ).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Among a set of solutions P , the nondominated set of solutions P \u2032 are those that are not dominated by any member of the set P . The nondominated set of the entire search space S is called the globally Pareto-optimal set. In general, a MOO algorithm usually admits a set of solutions not dominated by any solution encountered by it.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Genetic algorithms (GAs) are known to be more effective than classical methods such as weighted metrics, goal programming (Deb, 2001) , for solving MOO primarily because of their populationbased nature. A particularly popular GA of this type is NSGA-II (Deb et al., 2002) . In NSGA-II, initially a random parent population P 0 is created and the population is sorted based on the partial order defined by the non-domination relation. This results in a sequence of nondominated fronts. Each solution is assigned a fitness value which is equal to its non-domination level in the partial order. A child population Q 0 of size N is then created from the parent population P 0 by using binary tournament selection, recombination, and mutation operators. In general, in the t th iteration, a combined population R t = P t + Q t is formed. The size of R t is 2N , as the size of both P t and Q t is N . All the solutions of R t are sorted according to non-domination. If the total number of solutions belonging to the best nondominated set F 1 is smaller than N , then F 1 is to- Figure 1 : Chromosome representation for GA based feature selection tally included in P (t+1) . The remaining members of the population P (t+1) are chosen from the subsequent nondominated fronts in the order of their ranking. To choose exactly N solutions, the solutions of the last included front are sorted using the crowded comparison operator (Deb et al., 2002) and the best among them (i.e., those with lower crowding distance) are selected to fill in the available slots in P (t+1) . The new population P (t+1) is then used for selection, crossover and mutation to create a population Q (t+1) of size N .", | |
| "cite_spans": [ | |
| { | |
| "start": 122, | |
| "end": 133, | |
| "text": "(Deb, 2001)", | |
| "ref_id": "BIBREF3" | |
| }, | |
| { | |
| "start": 253, | |
| "end": 271, | |
| "text": "(Deb et al., 2002)", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 1420, | |
| "end": 1438, | |
| "text": "(Deb et al., 2002)", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 1073, | |
| "end": 1081, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Nondominated Sorting Genetic Algorithm-II (NSGA-II)", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Below we discuss how single and multi-objective optimization techniques can be used feature selection in the anaphora resolution task.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Two Algorithms for Feature Selection in Anaphora Resolution", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "If the total number of features is F , then the length of the chromosome is F . As an example, the encoding of a particular chromosome is represented in Figure 1 . Here F = 12 (i.e., total 12 different features are available). The chromosome represents the use of 7 features for constructing a classifier (first, third, fourth, seventh, tenth, eleventh and twelfth features). The entries of each chromosome are randomly initialized to either 0 or 1. Here, if the i th position of a chromosome is 0 then it represents that i th feature does not participate in constructing the classifier. Else if it is 1 then the i th feature participates in constructing the classifier.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 153, | |
| "end": 161, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Chromosome Representation and Population Initialization", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "For fitness computation, the following procedure is executed:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fitness Computation", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "1. Suppose there are N number of features present in a particular chromosome (i.e., there are total N number of 1's in that chromosome).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fitness Computation", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "2. Construct the coreference resolution system (i.e., BART) with only these N features.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fitness Computation", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "3. This coreference system is evaluated on the development data. The recall, precision and F-measure values of three metrics are calculated.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fitness Computation", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In case of single objective optimization (SOO), the objective function corresponding to a particular chromosome is the F-measure value of a single metric. This objective function is optimized using the search capability of GA. For MOO, the objective functions corresponding to a particular chromosome are F M U C (for the MUC metric), F \u03c6 3 (for CEAF using the \u03c6 3 entity alignment function (Luo, 2005) ) and F \u03c6 4 (for CEAF using the \u03c6 4 entity alignment function). These three objective functions are simultaneously optimized using the search capability of NSGA-II.", | |
| "cite_spans": [ | |
| { | |
| "start": 391, | |
| "end": 402, | |
| "text": "(Luo, 2005)", | |
| "ref_id": "BIBREF9" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fitness Computation", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In case of SOO, a single point crossover operation is used with a user defined crossover probability, \u00b5 c . A mutation operator is applied to each entry of the chromosome with a mutation probability, \u00b5 m , where the entry is randomly replaced by either 0 or 1. In this approach, the processes of fitness computation, selection, crossover, and mutation are executed for a maximum number of generations. The best string seen up to the last generation provides the solution to the above feature selection problem. Elitism has been implemented at each generation by preserving the best string seen upto that generation in a location outside the population. Thus on termination, this location contains the best feature combination.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Genetic Operators", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "We use crowded binary tournament selection as in NSGA-II, followed by conventional crossover and mutation for the MOO based feature selection. The most characteristic part of NSGA-II is its elitism operation, where the non-dominated solutions (Deb, 2001 ) among the parent and child populations are propagated to the next generation. The near-Pareto-optimal strings of the last generation provide the different solutions to the feature selection problem.", | |
| "cite_spans": [ | |
| { | |
| "start": 243, | |
| "end": 253, | |
| "text": "(Deb, 2001", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Genetic Operators", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "For our experiments, we use BART (Versley et al., 2008) , a modular toolkit for anaphora reso-lution that supports state-of-the-art statistical approaches to the task and enables efficient feature engineering. BART implements different models of anaphora resolution (mention-pair and entitymention; best-first vs. ranking), has interfaces to different machine learners (MaxEnt, SVM, decision trees) and provides a large set of linguistically motivated features, along with the possibility to design new ones. It is thus ideally suited for experimenting with optimization and feature selection.", | |
| "cite_spans": [ | |
| { | |
| "start": 33, | |
| "end": 55, | |
| "text": "(Versley et al., 2008)", | |
| "ref_id": "BIBREF20" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The BART System", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "In this study, we specifically focus on feature selection. 2 The complete list of features currently implemented in BART is listed in Table 1 ; all were considered in the present experiments. We used a simple mention-pair model without ranking as in (Soon et al., 2001 ). In the mention-pair model, anaphora resolution is recast as a binary classification problem. Each classification instance consists of two mentions, i.e. an anaphor M j and its potential antecedent M i (i < j). Instances are modeled as feature vectors (cf. Table 1 ) and are handed over to a binary classifier that decides, whether the anaphor and its candidate antecedent are mentions of the same entity or not. All the feature values are computed automatically.", | |
| "cite_spans": [ | |
| { | |
| "start": 250, | |
| "end": 268, | |
| "text": "(Soon et al., 2001", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 134, | |
| "end": 141, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| }, | |
| { | |
| "start": 528, | |
| "end": 535, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "The BART System", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "We train a maximum entropy classifier and follow the approach of (Soon et al., 2001) to partition mentions into coreference sets given the classifier's decisions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The BART System", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "We evaluated our approach on the ACE-02 dataset, which is divided in three subsets: bnews, npaper, and nwire. We provide results for both gold (handannotated) versions of the datasets (gbnews, gnpaper, gnwire) and system mentions extracted with CARAFE 3 (cbnews, cnpaper, cnwire). Table 2 compares the performance level obtained using all the features in Table 1 with that of a loose re-implementation of the system proposed by Soon et al. (2001) , commonly used as baseline and relying only on very shallow information. Our reimplementation of the Soon et al. model uses only a subset of features: those marked with an asterisk in Table 1 . We also provide in Table 2 typical state-of-the-art figures on the ACE-02 dataset, as presented in an overview by Poon and Domin- Soon et al. (2001) 71.6 67.2 69.6 67.8 62.6 67.5 66.7 67.9 69.7 All features (Table 1) 75 Soon et al. (2001) 61.3 56.7 55.9 63.3 57.6 54.0 60.8 58.2 57.0 All features (Table 1) 62.3 57.9 57.5 65.5 55.9 52.7 60.6 56.8 55.6 (Uryupina, 2007; Bengtson and Roth, 2008) , the set of features already included in BART is sufficient to achieve results well above the state of the art on the dataset we used.", | |
| "cite_spans": [ | |
| { | |
| "start": 428, | |
| "end": 446, | |
| "text": "Soon et al. (2001)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 772, | |
| "end": 790, | |
| "text": "Soon et al. (2001)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 862, | |
| "end": 880, | |
| "text": "Soon et al. (2001)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 994, | |
| "end": 1010, | |
| "text": "(Uryupina, 2007;", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 1011, | |
| "end": 1035, | |
| "text": "Bengtson and Roth, 2008)", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 281, | |
| "end": 288, | |
| "text": "Table 2", | |
| "ref_id": "TABREF1" | |
| }, | |
| { | |
| "start": 355, | |
| "end": 362, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| }, | |
| { | |
| "start": 632, | |
| "end": 639, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| }, | |
| { | |
| "start": 661, | |
| "end": 668, | |
| "text": "Table 2", | |
| "ref_id": "TABREF1" | |
| }, | |
| { | |
| "start": 849, | |
| "end": 858, | |
| "text": "(Table 1)", | |
| "ref_id": "TABREF0" | |
| }, | |
| { | |
| "start": 939, | |
| "end": 948, | |
| "text": "(Table 1)", | |
| "ref_id": "TABREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "The Data Sets", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "F M U C F \u03c6 3 F \u03c6 4 F M U C F \u03c6 3 F \u03c6 4 F M U C F \u03c6 3 F \u03c6 4 following", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Data Sets", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "F M U C F \u03c6 3 F \u03c6 4 F M U C F \u03c6 3 F \u03c6 4 F M U C F \u03c6 3 F \u03c6 4 following", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Data Sets", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "The results in Table 2 also confirm the intuition that, contrary to what is suggested by some of the early papers (Soon et al., 2001; Ng and Cardie, 2002) working on smaller datasets, linguistic factors do play a crucial role in anaphora resolution and therefore rich feature sets may lead to performance improvements once larger datasets are considered (a similar result was also obtained by Bengtson and Roth (2008) ). Such improvements, however, come at high costs, as both using larger datasets and larger sets of features learning a model becomes slower and requires much more memory.", | |
| "cite_spans": [ | |
| { | |
| "start": 114, | |
| "end": 133, | |
| "text": "(Soon et al., 2001;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 134, | |
| "end": 154, | |
| "text": "Ng and Cardie, 2002)", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 393, | |
| "end": 417, | |
| "text": "Bengtson and Roth (2008)", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 15, | |
| "end": 22, | |
| "text": "Table 2", | |
| "ref_id": "TABREF1" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "The Data Sets", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "This suggests that automatic feature selection may be essential not just to improve performance but also to be able to train a model-i.e., that an efficient coreference resolution system should combine rich linguistic feature sets with automatic feature selection mechanisms.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Data Sets", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "We set the following parameter values for both single (i.e., GA) and MOO (i.e., NSGA-II): population size=20, number of generations=30, probability of mutation \u00b5 m = 0.2 and probability of crossover \u00b5 c = 0.9. Both approaches are executed on devel-opment data to determine the optimal feature vector(s). Final results are reported on the test data. It is to be noted that GA is a stochastic approach and outputs different results for trials with different seeds and initial populations. Initial seeds and population are chosen randomly. Thus for each data set we executed the proposed single and multi objective based approaches 3 times. Finally, we report the maximum values of these 3 runs.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Genetic Algorithm Parameter Setting", | |
| "sec_num": "5.3" | |
| }, | |
| { | |
| "text": "Single objective GA based feature selection was executed on the six data sets to determine the appropriate set of features. For each data set three sets of experiments were carried out by optimizing the F-measure values of the three different evaluation metrics. The binary-valued feature vectors identified by the single objective GA based feature selection technique for the six data sets and the corresponding F-measure values are shown in Table 3. The order of the features in the vector corresponds to their order in Table 1 ; the values of 0's and 1's represent the absence and presence of the corresponding features. Significant improvements over the classifier based on all the features are indicated with \u2020 (sign test, p < 0.05).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 522, | |
| "end": 529, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Single Objective Optimization", | |
| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "These results show that for all the datasets, the proposed single objective GA-based feature selection technique performs better than the baseline approach of using all features. Moreover, the results show that the technique based on SOO (i.e., conventional GA-based method) with different objective functions provides different evaluation figures. Thus, it is meaningful to optimize each objective function separately.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Single Objective Optimization", | |
| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "It is also evident from Table 3 that the optimal feature set obtained by optimizing a single objective function may not be optimal with respect to another objective function. Thus, it is not possible to come up with common patterns in the set of optimal features. For example, in case of gbnews, the F-measure value of the first metric, i.e. of M U C corresponding to the optimal feature vector optimizing second metric, i.e. \u03c6 3 is 76.7. This is obviously less than the evaluation figure obtained by optimizing the first metric.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 24, | |
| "end": 31, | |
| "text": "Table 3", | |
| "ref_id": "TABREF3" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Single Objective Optimization", | |
| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "Thereafter we apply our proposed MOO based feature selection technique on the six data sets. The MOO approach provides a set of non-dominated solutions on the final Pareto optimal front. All the solutions are equally important from the algorithmic point of view. In Table 4 , we show the final solutions obtained by the MOO based approach for all the data sets. Significant improvements over the classifier based on all the features are indicated with \u2020 (sign test, p < 0.05).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 266, | |
| "end": 273, | |
| "text": "Table 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "6.2" | |
| }, | |
| { | |
| "text": "The results in Table 4 indicate that the MOO based technique achieves higher performance than the single objective GA based approach. For the gbnews data set, MOO achieves 0.6, 0.3 and 0.8 Fmeasure points increments for three metrics over the single objective GA based technique. For the gnpaper data set, there are increments of 2.5 F-measure points on second metric and 1.0 Fmeasure point on third metric over the corresponding single objective GA based technique. Similarly, for all other datasets the MOO based approach attains superior performance over the SOObased approach.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 15, | |
| "end": 22, | |
| "text": "Table 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Multi-objective Optimization", | |
| "sec_num": "6.2" | |
| }, | |
| { | |
| "text": "As discussed in Section 2 most work on optimization in anaphora resolution relies on manual optimization; the one significant exception is the work of Hoste (2005) .", | |
| "cite_spans": [ | |
| { | |
| "start": 151, | |
| "end": 163, | |
| "text": "Hoste (2005)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Comparison with Related Work", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "There are two major differences between the approach of Hoste (2005) and that followed in our study. First, the scope of (Hoste, 2005 ) is restricted to single-objective optimization. As we saw above, this might provide unstable solutions, that are too tailored to a particular scoring metric. Second, the feature set of Hoste (2005) is relatively small and therefore does not provide an efficient test-bed for a feature selection approach. Not surprising, parameter optimization shows a more consistent effect on the overall performance than feature selection in (Hoste, 2005) 's experiments.", | |
| "cite_spans": [ | |
| { | |
| "start": 56, | |
| "end": 68, | |
| "text": "Hoste (2005)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 121, | |
| "end": 133, | |
| "text": "(Hoste, 2005", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 321, | |
| "end": 333, | |
| "text": "Hoste (2005)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 564, | |
| "end": 577, | |
| "text": "(Hoste, 2005)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Comparison with Related Work", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "In this paper we showed that it may not be necessary to choose one among the existing metrics for anaphora resolution-in fact, that developing systems attempting to optimize according to a combination of them may lead to better results.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion and Conclusions", | |
| "sec_num": "8" | |
| }, | |
| { | |
| "text": "In subsequent work, we plan to expand the optimization technique to consider also learning parameters optimization, classifier selection, and learning model selection.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion and Conclusions", | |
| "sec_num": "8" | |
| }, | |
| { | |
| "text": "We use the term 'anaphora resolution' to refer to the task perhaps most commonly referred to as 'coreference resolution,' which many including us find a misnomer. For the purposes of the present paper the two terms could be seen as interchangeable.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The choice of the best model and the best machine learner, along with its parameters, is the main direction of our future work.3 http://sourceforge.net/projects/ carafe", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Algorithms for scoring coreference chains", | |
| "authors": [ | |
| { | |
| "first": "Amit", | |
| "middle": [], | |
| "last": "References", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Breck", | |
| "middle": [], | |
| "last": "Bagga", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Baldwin", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1998, | |
| "venue": "Proc. of the LREC workshop on Linguistic Coreference", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "563--566", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "References Amit Bagga and Breck Baldwin. 1998. Algorithms for scoring coreference chains. In Proc. of the LREC workshop on Linguistic Coreference, pages 563- 566, Granada.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Understanding the value of features for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Eric", | |
| "middle": [], | |
| "last": "Bengtson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dan", | |
| "middle": [], | |
| "last": "Roth", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2008, | |
| "venue": "Proc. of EMNLP", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Eric Bengtson and Dan Roth. 2008. Understanding the value of features for coreference resolution. In Proc. of EMNLP.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "A fast and elitist multiobjective genetic algorithm: NSGA-II", | |
| "authors": [ | |
| { | |
| "first": "Kalyanmoy", | |
| "middle": [], | |
| "last": "Deb", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Amrit", | |
| "middle": [], | |
| "last": "Pratap", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sameer", | |
| "middle": [], | |
| "last": "Agarwal", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "T", | |
| "middle": [], | |
| "last": "Meyarivan", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2002, | |
| "venue": "IEEE Transactions on Evolutionary Computation", | |
| "volume": "6", | |
| "issue": "2", | |
| "pages": "181--197", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):181-197.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Multi-objective Optimization Using Evolutionary Algorithms", | |
| "authors": [ | |
| { | |
| "first": "Kalyanmoy", | |
| "middle": [], | |
| "last": "Deb", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Kalyanmoy Deb. 2001. Multi-objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Ltd, England.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "The automatic content extraction (ACE) program-tasks, data, and evaluation", | |
| "authors": [ | |
| { | |
| "first": "George", | |
| "middle": [], | |
| "last": "Doddington", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Alexis", | |
| "middle": [], | |
| "last": "Mitchell", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mark", | |
| "middle": [], | |
| "last": "Przybocki", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lance", | |
| "middle": [], | |
| "last": "Ramshaw", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Stephanie", | |
| "middle": [], | |
| "last": "Strassell", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Weischedel", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proc. of LREC", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "George Doddington, Alexis Mitchell, Mark Przybocki, Lance Ramshaw, Stephanie Strassell, and Ralph Weischedel. 2004. The automatic content extrac- tion (ACE) program-tasks, data, and evaluation. In Proc. of LREC.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "Genetic Algorithms in Search, Optimization and Machine Learning", | |
| "authors": [ | |
| { | |
| "first": "David", | |
| "middle": [ | |
| "E" | |
| ], | |
| "last": "Goldberg", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1989, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "Optimization Issues in Machine Learning of Coreference Resolution", | |
| "authors": [ | |
| { | |
| "first": "Veronique", | |
| "middle": [], | |
| "last": "Hoste", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Veronique Hoste. 2005. Optimization Issues in Ma- chine Learning of Coreference Resolution. Ph.D. thesis, Antwerp University.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "Incorporating contextual cues in trainable models for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Ryu", | |
| "middle": [], | |
| "last": "Iida", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kentaro", | |
| "middle": [], | |
| "last": "Inui", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hiroya", | |
| "middle": [], | |
| "last": "Takamura", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yuji", | |
| "middle": [], | |
| "last": "Matsumoto", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2003, | |
| "venue": "Proc. EACL Workshop on the Computational Treatment of Anaphora", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ryu Iida, Kentaro Inui, Hiroya Takamura, and Yuji Matsumoto. 2003. Incorporating contextual cues in trainable models for coreference resolution. In Proc. EACL Workshop on the Computational Treatment of Anaphora.", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "A mentionsynchronous coreference resolution algorithm based on the Bell Tree", | |
| "authors": [ | |
| { | |
| "first": "Xiaoqiang", | |
| "middle": [], | |
| "last": "Luo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Abe", | |
| "middle": [], | |
| "last": "Ittycheriah", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hongyan", | |
| "middle": [], | |
| "last": "Jing", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nanda", | |
| "middle": [], | |
| "last": "Kambhatla", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Salim", | |
| "middle": [], | |
| "last": "Roukos", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proc. of ACL", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "136--143", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiaoqiang Luo, Abe Ittycheriah, Hongyan Jing, Nanda Kambhatla, and Salim Roukos. 2004. A mention- synchronous coreference resolution algorithm based on the Bell Tree. In Proc. of ACL 2004, pages 136- 143.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "On coreference resolution performance metrics", | |
| "authors": [ | |
| { | |
| "first": "Xiaoqiang", | |
| "middle": [], | |
| "last": "Luo", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Proc. NAACL / EMNLP", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiaoqiang Luo. 2005. On coreference resolution per- formance metrics. In Proc. NAACL / EMNLP, Van- couver.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "Optimizing to arbitrary NLP metrics using ensemble selection", | |
| "authors": [ | |
| { | |
| "first": "Art", | |
| "middle": [], | |
| "last": "Munson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Claire", | |
| "middle": [], | |
| "last": "Cardie", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rich", | |
| "middle": [], | |
| "last": "Caruana", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Proceedings of HLT/EMNLP", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "539--546", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Art Munson, Claire Cardie, and Rich Caruana. 2005. Optimizing to arbitrary NLP metrics using ensem- ble selection. In Proceedings of HLT/EMNLP, pages 539-546.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Improving machine learning approaches to coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Vincent", | |
| "middle": [], | |
| "last": "Ng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Claire", | |
| "middle": [], | |
| "last": "Cardie", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2002, | |
| "venue": "Proceedings of the 40th Annual Meeting on Association for C omputational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "104--111", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Vincent Ng and Claire Cardie. 2002. Improving ma- chine learning approaches to coreference resolution. In Proceedings of the 40th Annual Meeting on Asso- ciation for C omputational Linguistics, pages 104- 111.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Joint unsupervised coreference resolution with Markov logic", | |
| "authors": [ | |
| { | |
| "first": "Hoifung", | |
| "middle": [], | |
| "last": "Poon", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pedro", | |
| "middle": [], | |
| "last": "Domingos", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2008, | |
| "venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Hoifung Poon and Pedro Domingos. 2008. Joint unsupervised coreference resolution with Markov logic. In Proceedings of the Conference on Empiri- cal Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "A deeper look into features for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Marta", | |
| "middle": [], | |
| "last": "Recasens", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Eduard", | |
| "middle": [], | |
| "last": "Hovy", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2009, | |
| "venue": "Anaphora Processing and Applications (DAARC 2009, number 5847 in LNAI", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "29--42", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Marta Recasens and Eduard Hovy. 2009. A deeper look into features for coreference resolution. In S. Lalitha Devi, A. Branco, and R. Mitkov, editors, Anaphora Processing and Applications (DAARC 2009, number 5847 in LNAI, pages 29-42, Berlin / Heidelberg. Springer-Verlag.", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Blanc: Implementing the rand index for coreference evaluation", | |
| "authors": [ | |
| { | |
| "first": "Marta", | |
| "middle": [], | |
| "last": "Recasens", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Eduard", | |
| "middle": [], | |
| "last": "Hovy", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2011, | |
| "venue": "Natural Language Engineering", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Marta Recasens and Eduard Hovy. 2011. Blanc: Im- plementing the rand index for coreference evalua- tion. Natural Language Engineering.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "Semeval-2010 task 1: Coreference resolution in multiple languages", | |
| "authors": [], | |
| "year": null, | |
| "venue": "Proc. SEMEVAL 2010", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Semeval-2010 task 1: Coreference resolution in multiple languages. In Proc. SEMEVAL 2010, Upp- sala.", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "A machine learning approach to coreference resolution of noun phrases", | |
| "authors": [], | |
| "year": 2001, | |
| "venue": "Computational Linguistic", | |
| "volume": "27", | |
| "issue": "4", | |
| "pages": "521--544", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. 2001. A machine learning ap- proach to coreference resolution of noun phrases. Computational Linguistic, 27(4):521-544.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Knowledge Acquisition for Coreference Resolution", | |
| "authors": [ | |
| { | |
| "first": "Olga", | |
| "middle": [], | |
| "last": "Uryupina", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2007, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Olga Uryupina. 2007. Knowledge Acquisition for Coreference Resolution. Ph.D. thesis, University of the Saarland.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "Corry: a system for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Olga", | |
| "middle": [], | |
| "last": "Uryupina", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "Proceedings of the 5th International Workshop on Semantic Eval uation (SemEval'10)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Olga Uryupina. 2010. Corry: a system for coreference resolution. In Proceedings of the 5th International Workshop on Semantic Eval uation (SemEval'10).", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "BART: a modular toolkit for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Yannick", | |
| "middle": [], | |
| "last": "Versley", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Simone", | |
| "middle": [ | |
| "Paolo" | |
| ], | |
| "last": "Ponzetto", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Massimo", | |
| "middle": [], | |
| "last": "Poesio", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Vladimir", | |
| "middle": [], | |
| "last": "Eidelman", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Alan", | |
| "middle": [], | |
| "last": "Jern", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jason", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Xiaofeng", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Alessandro", | |
| "middle": [], | |
| "last": "Moschitti", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2008, | |
| "venue": "Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "9--12", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yannick Versley, Simone Paolo Ponzetto, Massimo Poesio, Vladimir Eidelman, Alan Jern, Jason Smith, Xiaofeng Yang, and Alessandro Moschitti. 2008. BART: a modular toolkit for coreference resolution. In Proceedings of the 46th Annual Meeting of the As- sociation for Computational Linguistics on Human Language Technologies, pages 9-12.", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "A modeltheoretic coreference scoring scheme", | |
| "authors": [ | |
| { | |
| "first": "Marc", | |
| "middle": [], | |
| "last": "Vilain", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Burger", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Aberdeen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1995, | |
| "venue": "Proc. of the Sixth Message Understanding Conference", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "45--52", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Marc Vilain, John Burger, John Aberdeen, Dennis Con- nolly, and Lynette Hirschman. 1995. A model- theoretic coreference scoring scheme. In Proc. of the Sixth Message Understanding Conference, pages 45-52.", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "A twin-candidate model of coreference resolution with non-anaphor identification capability", | |
| "authors": [ | |
| { | |
| "first": "Xiaofeng", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jian", | |
| "middle": [], | |
| "last": "Su", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chew Lim", | |
| "middle": [], | |
| "last": "Tan", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2005, | |
| "venue": "Proc. of IJCNLP", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "719--730", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiaofeng Yang, Jian Su, and Chew Lim Tan. 2005. A twin-candidate model of coreference resolution with non-anaphor identification capability. In Proc. of IJCNLP, pages 719-730.", | |
| "links": null | |
| }, | |
| "BIBREF23": { | |
| "ref_id": "b23", | |
| "title": "Maximum metric score training for coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Shanheng", | |
| "middle": [], | |
| "last": "Zhao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hwee Tou", | |
| "middle": [], | |
| "last": "Ng", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "Proceeding of COLING-2010", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Shanheng Zhao and Hwee Tou Ng. 2010. Maximum metric score training for coreference resolution. In Proceeding of COLING-2010.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "TABREF0": { | |
| "type_str": "table", | |
| "text": "Features used by BART: each feature describes a pair of mentions {M i , M j }, i < j, where M i is a candidate antecedent and M j is a candidate anaphor Mi and Mj , as identified in Soon et al. Mi and Mj are in an apposition Appositive2 Mi and Mj are adjacent Coordination Mi is a coordination; same for Mj HeadPartOfSpeech POS of Mi's head; same for Mj and the pair SynPos depth of Mi's node in the parse tree Attributes Mi and Mj have incompatible premodifiers Relations Mi and Mj have incompatible postmodifiers Matching StringMatch* Mi and Mj have the same surface form after stripping off the determiners NonPro StringMatch both Mi and Mj are non-pronominal and Stringmatch(Mi, Mj ) == 1 Pro StringMatch both Mi and Mj are pronominal and Stringmatch(Mi, Mj ) == 1 NE StringMatch both Mi and Mj are NE and Stringmatch(Mi, Mj ) == 1 HeadMatch Mi and Mj have the same head MinSame Mi and Mj have the same minimal span LeftRightMatch Mi, number of preceding mentions with the same head lemma Web Wiki Mi and Mj have the same wikipedia entry Yago Mi and Mj are linked in Yago via means or typeof relation WebPatterns specific contexts for co-reference extracted from the web Proximity DistanceMarkable distance in mentions between Mi and Mj DistanceSentenceInt* distance in sentences between Mi and Mj DistanceSentence log-distance in sentences between Mi and Mj DistanceSentence2 log-distance in sentences between Mi and Mj , different formula DistDiscrete distance in sentences between Mi and Mj discretized into {0,1,>=2} Miscellaneous SpeechMi is in quoted speech; same for Mj and the pair", | |
| "content": "<table><tr><td/><td>Mention types and subtypes</td></tr><tr><td colspan=\"2\">MentionType* relevant types of MentionType Ante Salient</td><td>Mi is demonstrative; Mi is an NE</td></tr><tr><td>MentionType Ante Extra</td><td/><td>Mi is a pronoun</td></tr><tr><td>MentionType Ana</td><td colspan=\"2\">Mj is a definite, demonstrative or indefinite NP, or pronoun of a specific type</td></tr><tr><td>MentionType2</td><td colspan=\"2\">relevant types of Mi and Mj , as identified in Soon et al.</td></tr><tr><td>MentionType Salience</td><td colspan=\"2\">combination of M entionT ype and M entionT ype Ana</td></tr><tr><td>FirstSecondPerson</td><td colspan=\"2\">Mi is a pronoun of the 1st/second person, same for Mj</td></tr><tr><td>PronounLeftRight PronounWordForm</td><td colspan=\"2\">4 possible values for < Mi is a pronoun> * < Mj is a pronoun > lemma for Mi if it's a pronoun; same for Mj</td></tr><tr><td>SemClassValue</td><td colspan=\"2\">semantic class of Mi, and Mj and the pair</td></tr><tr><td>BothLocation</td><td colspan=\"2\">both Mi and Mj are locations or geo-political</td></tr><tr><td/><td>Agreement</td></tr><tr><td>GenderAgree*</td><td/><td>Mi and Mj agree in gender</td></tr><tr><td>NumberAgree*</td><td/><td>Mi and Mj agree in number</td></tr><tr><td>AnimacyAgree*</td><td/><td>Mi and Mj agree in animacy</td></tr><tr><td/><td>Aliasing</td></tr><tr><td>Alias*</td><td/><td>heuristical NE-matching</td></tr><tr><td>BetterNames</td><td colspan=\"2\">heuristical matching for personal names</td></tr><tr><td/><td>Syntax</td></tr><tr><td>Appositive*</td><td/></tr><tr><td/><td colspan=\"2\">Mj is a prefix or suffix substring of Mi or vice versa</td></tr><tr><td>StringMatchExtra</td><td/><td>extra string-macthing for bare plurals</td></tr><tr><td>StringKernel</td><td/><td>approximate matching</td></tr><tr><td/><td>Salience</td></tr><tr><td>First Mention</td><td/><td>Mi is the first mention in its sentence</td></tr><tr><td>CorefChain</td><td colspan=\"2\">Size of the coreference chain suggested for Mi so far (with a threshold)</td></tr><tr><td>NonProSalience</td><td>for non-pronominal</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF1": { | |
| "type_str": "table", | |
| "text": "Baseline performance on the ACE-02 dataset", | |
| "content": "<table><tr><td/><td>gold mentions</td><td/></tr><tr><td>gbnews</td><td>gnpaper</td><td>gnwire</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "type_str": "table", | |
| "text": "Feature vectors identified via single-objective optimization.", | |
| "content": "<table><tr><td colspan=\"2\">DataSet Metric</td><td>Features Selected</td><td colspan=\"2\">F M U C F \u03c6 3</td><td>F \u03c6 4</td></tr><tr><td/><td>opt.</td><td/><td/></tr><tr><td colspan=\"2\">gbnews MUC</td><td colspan=\"2\">00100110110111100111111000111001001111111010 76.8</td><td>71.5 74.5</td></tr><tr><td/><td>\u03c6 3 ,\u03c6 4</td><td colspan=\"2\">10011000111010110000101101010011011011000001 76.7</td><td>71.8 \u2020 74.9 \u2020</td></tr><tr><td colspan=\"2\">gnpaper MUC</td><td colspan=\"2\">10000001001111110101011101110000101010100111 74.6</td><td>67.1 70.1 \u2020</td></tr><tr><td/><td>\u03c6 3</td><td colspan=\"2\">10101001100100110100100000010100010001101100 72.2</td><td>67.6 69.1</td></tr><tr><td/><td>\u03c6 4</td><td colspan=\"2\">11101001100100110100111000100101110010001100 71.4</td><td>65.2 70.3</td></tr><tr><td>gnwire</td><td>MUC</td><td colspan=\"2\">10111011011111110010101010011010011011001011 74.0 \u2020</td><td>70.3 \u2020 73.1 \u2020</td></tr><tr><td/><td>\u03c6 3</td><td colspan=\"2\">11011011100001000011110110101111011110001101 71.4</td><td>72.3 73.6</td></tr><tr><td/><td>\u03c6 4</td><td colspan=\"2\">11101001100100110100111000100101110010001100 71.7</td><td>72.1 74.4</td></tr><tr><td>cbnews</td><td colspan=\"3\">MUC,\u03c6 3 11111001100101000011011100101101101111001100 64.6</td><td>59.7 58.4</td></tr><tr><td/><td>\u03c6 4</td><td colspan=\"2\">11111001100001000011110100101111101110001101 63.6</td><td>59.6 58.8</td></tr><tr><td colspan=\"4\">cnpaper MUC,\u03c6 3 01000100100101011001000010111100101100001000 66.5 \u03c6 4 10100101101011100011111110010100100010010011 66.2</td><td>59.7 \u2020 54.7 \u2020 59.1 55.6 \u2020</td></tr><tr><td>cnwire</td><td>MUC</td><td colspan=\"2\">00101111101110101001100000010101001011001001 63.8</td><td>60.0 58.1</td></tr><tr><td/><td>\u03c6 3 ,\u03c6 4</td><td colspan=\"2\">00011000101110100010000010011000100110000100 63.4</td><td>61.2 58.4</td></tr><tr><td colspan=\"3\">gos (2008). The results clearly show that although</td><td/></tr><tr><td colspan=\"3\">even larger sets of features have been proposed</td><td/></tr></table>", | |
| "num": null, | |
| "html": null | |
| } | |
| } | |
| } | |
| } |