Graph-Cut-Based Anaphoricity Determination for Coreference Resolution

Recent work has shown that explicitly identifying and filtering non-anaphoric mentions prior to coreference resolution can improve the performance of a coreference system. We present a novel approach to this task of anaphoricity determination based on graph cuts, and demonstrate its superiority to competing approaches by comparing their effectiveness in improving a learning-based coreference system on the ACE data sets.

Use Generalized Representations, But Do Not Forget Surface Features

Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface features outperforms more complex neural models for detecting anaphoric mentions. Our analysis suggests that using generalized representations and surface features have different strength that should be both taken into account for improving coreference resolution.Comment: CORBON workshop@EACL 201

Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures

International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting

Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures

International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting

Structured learning with latent trees: a joint approach to coreference resolution

This thesis explores ways to define automated coreference resolution systems by using structured machine learning techniques. We design supervised models that learn to build coreference clusters from raw text: our main objective is to get model able to process documentsglobally, in a structured fashion, to ensure coherent outputs. Our models are trained and evaluated on the English part of the CoNLL-2012 Shared Task annotated corpus with standard metrics. We carry out detailed comparisons of different settings so as to refine our models anddesign a complete end-to-end coreference resolver. Specifically, we first carry out a preliminary work on improving the way features areemployed by linear models for classification: we extend existing work on separating different types of mention pairs to define more accurate classifiers of coreference links. We then define various structured models based on latent trees to learn to build clusters globally, andnot only from the predictions of a mention pair classifier. We study different latent representations (various shapes and sparsity) and show empirically that the best suited structure is some restricted class of trees related to the best-first rule for selecting coreference links. Wefurther improve this latent representation by integrating anaphoricity modelling jointly with coreference, designing a global (structured at the document level) and joint model outperforming existing models on gold mentions evaluation. We finally design a complete end-to-endresolver and evaluate the improvement obtained by our new models on detected mentions, a more realistic setting for coreference resolution

Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement LIMERIC, a state-of-the-art coreference resolution engine. LIMERIC naturally incorporates both non-local decoding and entity-level modelling to achieve the highly competitive benchmark performance of 64.22% and 59.99% on the CoNLL-2012 benchmark with a simple model and a baseline feature set. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Each contribution achieves statistically significant improvements and sum to gains of 1.65% and 1.66% on the CoNLL-2012 benchmark, yielding performance values of 65.76% and 61.27%. For each novel feature we propose, we contribute an accompanying analysis so as to better understand how cognitive theories apply to real language data. LIMERIC is at once a platform for exploring cognitive insights into coreference and a viable alternative to current systems. We are excited by the promise of incorporating our and further cognitive insights into more complex frameworks since this has the potential to both improve the performance of computational models, as well as our understanding of the mechanisms underpinning human reference resolution

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table

Structured Representations for Coreference Resolution

Coreference resolution is the task of determining which expressions in a text are used to refer to the same entity. This task is one of the most fundamental problems of natural language understanding. Inherently, coreference resolution is a structured task, as the output consists of sets of coreferring expressions. This complex structure poses several challenges since it is not clear how to account for the structure in terms of error analysis and representation. In this thesis, we present a treatment of computational coreference resolution that accounts for the structure. Our treatment encompasses error analysis and the representation of approaches to coreference resolution. In particular, we propose two frameworks in this thesis. The first framework deals with error analysis. We gather requirements for an appropriate error analysis method and devise a framework that considers a structured graph-based representation of the reference annotation and the system output. Error extraction is performed by constructing linguistically motivated or data-driven spanning trees for the graph-based coreference representations. The second framework concerns the representation of approaches to coreference resolution. We show that approaches to coreference resolution can be understood as predictors of latent structures that are not annotated in the data. From these latent structures, the final output is derived during a post-processing step. We devise a machine learning framework for coreference resolution based on this insight. In this framework, we have a unified representation of approaches to coreference resolution. Individual approaches can be expressed as instantiations of a generic approach. We express many approaches from the literature as well as novel variants in our framework, ranging from simple pairwise classification approaches to complex entity-centric models. Using the uniform representation, we are able to analyze differences and similarities between the models transparently and in detail. Finally, we employ the error analysis framework to perform a qualitative analysis of differences in error profiles of the models on a benchmark dataset. We trace back differences in the error profiles to differences in the representation. Our analysis shows that a mention ranking model and a tree-based mention-entity model with left-to-right inference have the highest performance. We discuss reasons for the improved performance and analyze why more advanced approaches modeled in our framework cannot improve on these models. An implementation of the frameworks discussed in this thesis is publicly available

A constraint-based hypergraph partitioning approach to coreference resolution

The objectives of this thesis are focused on research in machine learning for coreference resolution. Coreference resolution is a natural language processing task that consists of determining the expressions in a discourse that mention or refer to the same entity. The main contributions of this thesis are (i) a new approach to coreference resolution based on constraint satisfaction, using a hypergraph to represent the problem and solving it by relaxation labeling; and (ii) research towards improving coreference resolution performance using world knowledge extracted from Wikipedia. The developed approach is able to use entity-mention classi cation model with more expressiveness than the pair-based ones, and overcome the weaknesses of previous approaches in the state of the art such as linking contradictions, classi cations without context and lack of information evaluating pairs. Furthermore, the approach allows the incorporation of new information by adding constraints, and a research has been done in order to use world knowledge to improve performances. RelaxCor, the implementation of the approach, achieved results in the state of the art, and participated in international competitions: SemEval-2010 and CoNLL-2011. RelaxCor achieved second position in CoNLL-2011.La resolució de correferències és una tasca de processament del llenguatge natural que consisteix en determinar les expressions d'un discurs que es refereixen a la mateixa entitat del mon real. La tasca té un efecte directe en la minería de textos així com en moltes tasques de llenguatge natural que requereixin interpretació del discurs com resumidors, responedors de preguntes o traducció automàtica. Resoldre les correferències és essencial si es vol poder “entendre” un text o un discurs. Els objectius d'aquesta tesi es centren en la recerca en resolució de correferències amb aprenentatge automàtic. Concretament, els objectius de la recerca es centren en els següents camps: + Models de classificació: Els models de classificació més comuns a l'estat de l'art estan basats en la classificació independent de parelles de mencions. Més recentment han aparegut models que classifiquen grups de mencions. Un dels objectius de la tesi és incorporar el model entity-mention a l'aproximació desenvolupada. + Representació del problema: Encara no hi ha una representació definitiva del problema. En aquesta tesi es presenta una representació en hypergraf. + Algorismes de resolució. Depenent de la representació del problema i del model de classificació, els algorismes de ressolució poden ser molt diversos. Un dels objectius d'aquesta tesi és trobar un algorisme de resolució capaç d'utilitzar els models de classificació en la representació d'hypergraf. + Representació del coneixement: Per poder administrar coneixement de diverses fonts, cal una representació simbòlica i expressiva d'aquest coneixement. En aquesta tesi es proposa l'ús de restriccions. + Incorporació de coneixement del mon: Algunes correferències no es poden resoldre només amb informació lingüística. Sovint cal sentit comú i coneixement del mon per poder resoldre coreferències. En aquesta tesi es proposa un mètode per extreure coneixement del mon de Wikipedia i incorporar-lo al sistem de resolució. Les contribucions principals d'aquesta tesi son (i) una nova aproximació al problema de resolució de correferències basada en satisfacció de restriccions, fent servir un hypergraf per representar el problema, i resolent-ho amb l'algorisme relaxation labeling; i (ii) una recerca per millorar els resultats afegint informació del mon extreta de la Wikipedia. L'aproximació presentada pot fer servir els models mention-pair i entity-mention de forma combinada evitant així els problemes que es troben moltes altres aproximacions de l'estat de l'art com per exemple: contradiccions de classificacions independents, falta de context i falta d'informació. A més a més, l'aproximació presentada permet incorporar informació afegint restriccions i s'ha fet recerca per aconseguir afegir informació del mon que millori els resultats. RelaxCor, el sistema que ha estat implementat durant la tesi per experimentar amb l'aproximació proposada, ha aconseguit uns resultats comparables als millors que hi ha a l'estat de l'art. S'ha participat a les competicions internacionals SemEval-2010 i CoNLL-2011. RelaxCor va obtenir la segona posició al CoNLL-2010