Coreference Resolution via Hypergraph Partitioning

Coreference resolution is one of the most fundamental Natural Language Processing tasks, aiming to identify the coreference relation in texts. The task is to group mentions (i.e. phrases of interest) into sets, so that all mentions in one set refer to the same entity (i.e. a real world object). Mentions are conventionally proper names, common nouns and pronouns. Lately, the coreference task has been extended to deal with verb phrases too. However, we only work with noun phrase mentions in this thesis. By linking mentions together in a document, not only entities are recovered but also different fragments of the context are connected. This therefore leads to a better text understanding. Coreference resolution is essentially important to many applications, such as text summarization and information extraction. In this thesis, we propose a novel coreference model based on hypergraph partitioning. Our system is named COPA, standing for Coreference Partitioner. Given a raw document, COPA represents it as a hypergraph, upon which the hypergraph partitioning algorithms are applied to derive coreference sets directly. The coreference relation is a high-dimensional relation, because it depends on multiple types of basic relations (e.g. string similarities and semantic relatedness). Most of the previous work on the coreference resolution task combines the basic relations between mentions into single ones and derives the coreference sets afterward. Since it is relatively expensive to learn the combination of the basic relations, we propose a novel hypergraph representation model for coreference resolution. In our model, the mentions are taken as vertices in the hypergraph and the relational features derived from the basic relations as hyperedges. The hypergraph allows for multiple edges between vertices, so that it suits the high-dimension property of the coreference relation. Moreover, in a hypergraph one hyperedge can connect more than two vertices. As a result the hypergraph directly represents the relations between sets of mentions as required for the coreference resolution task. Since the basic relations are incorporated in an overlapping manner, COPA only needs a few training documents to achieve competitive performance. The weakly supervised nature makes COPA a good candidate when applying to different domains or languages, or when only limited training data is available. The inference of the coreference resolution task deals with sets of mentions. It needs to capture the relations between multiple mentions in order to derive the final coreference sets. Therefore, we consider coreference resolution as a set problem. Most of the previous coreference models address the set problem by dividing the resolution into two steps --- a classification step and a clustering step. The classification step makes decisions for each pair of mentions on whether they are coreferent or not. Upon the pairwise decisions, the clustering step further groups mentions into the final sets. The two-step division makes the classification performance not necessarily positively correlated with the end evaluation numbers. It is difficult to track the error propagation and hard to optimize with respect to the final coreference sets. Moreover, since the coreference decisions are made between pairs of mentions independently, global context information is missing in those models. In this thesis, we propose a global coreference model via hypergraph partitioning. We design two algorithms based on the spectral clustering technique --- a hierarchical R2 partitioner and a flat k-way flatK partitioner. We also propose extensions to the clustering algorithms of COPA, aiming to include constraints to enforce the cluster-level consistency. The constrained COPA is the first attempt towards a better learning scheme for our system. It solves the cluster-level inconsistency problem and at the same time contributes to research in the constrained graph clustering field. Since COPA is an end-to-end coreference system, the important implementation issues encountered when applying clustering algorithms to practical uses are also addressed in this thesis. For instance, the existing evaluation metrics become problematic when the automatically identified mentions do not align with the ones in the ground truth. In this thesis, we propose variants of the coreference evaluation metrics to tackle this problem. COPA outperforms several baseline systems in fair settings, using the same features and the same mentions and only comparing the effectiveness of the models themselves. It also performs competitively compared to the state-of-the-art systems across different evaluation metrics, different data sets and different domains

Hypernode Graphs for Learning from Binary Relations between Groups in Networks

International audienceThe aim of this paper is to propose methods for learning from interactions between groups in networks. We introduced hypernode graphs in Ricatte et al (2014) a formal model able to represent group interactions and able to infer individual properties as well. Spectral graph learning algorithms were extended to the case of hypern-ode graphs. As a proof-of-concept, we have shown how to model multiple players games with hypernode graphs and that spectral learning algorithms over hyper-node graphs obtain competitive results with skill ratings specialized algorithms. In this paper, we explore theoretical issues for hypernode graphs. We show that hypernode graph kernels strictly generalize over graph kernels and hypergraph kernels. We show that hypernode graphs correspond to signed graphs such that the matrix D − W is positive semidefinite. It should be noted that homophilic relations between groups may lead to non homophilic relations between individ-uals. Moreover, we also present some issues concerning random walks and the resistance distance for hypernode graphs

Apprentissage d'une hiérarchie de modèles à paires spécialisés pour la résolution de la coréférence

National audienceNous proposons une nouvelle méthode pour améliorer significativement la performance des modèles à paires de mentions pour la résolution de la coréférence. Étant donné un ensemble d'indicateurs, notre méthode apprend à séparer au mieux des types de paires de mentions en classes d'équivalence, chacune de celles-ci donnant lieu à un modèle de classification spécifique. La procédure algorithmique proposée trouve le meilleur espace de traits (créé à partir de combinaisons de traits élémentaires et d'indicateurs) pour discriminer les paires de mentions coréférentielles. Bien que notre approche explore un très vaste ensemble d'espaces de trait, elle reste efficace en exploitant la structure des hiérarchies construites à partir des indicateurs. Nos expériences sur les données anglaises de la CoNLL-2012 Shared Task indiquent que notre méthode donne des gains de performance par rapport au modèle initial utilisant seulement les traits élémentaires, et ce, quelque soit la méthode de formation des chaînes ou la métrique d'évaluation choisie. Notre meilleur système obtient une moyenne de 67.2 en F1-mesure MUC, B3 et CEAF ce qui, malgré sa simplicité, le situe parmi les meilleurs systèmes testés sur ces données

Clustering Spectral avec Contraintes de Paires réglées par Noyaux Gaussiens

International audienceRésumé Nous considérons le problème du clustering spectral partielle-ment supervisé par des contraintes de la forme « must-link » et « cannot-link ». De telles contraintes apparaissent fréquemment dans divers pro-blèmes, comme la résolution de la coréférence en traitement automatique du langage naturel. L'approche développée dans ce papier consiste à ap-prendre une nouvelle représentation de l'espace pour les données, ainsi qu'une nouvelle distance dans cet espace. Cette représentation est ob-tenue via une transformation linéaire de l'enveloppe spectrale des don-nées. Les contraintes sont exprimées avec des fonctions Gaussiennes qui réajustent localement les similarités entre les objets. Un problème d'op-timisation global et non convexe est alors obtenu et l'apprentissage du modèle se fait grâce à des techniques de descentes de gradient. Nous évaluons notre algorithme sur des jeux de données standards et le com-parons à divers algorithmes de l'état de l'art, comme [14,18,32]. Les ré-sultats sur ces jeux de données, ainsi que sur le jeu de données de la tâche de coréférence CoNLL-2012, montrent que notre algorithme amé-liore significativement la qualité des clusters obtenus par les précédentes approches, et est plus robuste en montée en charge

Neural Coreference Resolution for Turkish

Coreference resolution deals with resolving mentions of the same underlying entity in a given text. This challenging task is an indispensable aspect of text understanding and has important applications in various language processing systems such as question answering and machine translation. Although a significant amount of studies is devoted to coreference resolution, the research on Turkish is scarce and mostly limited to pronoun resolution. To our best knowledge, this article presents the first neural Turkish coreference resolution study where two learning-based models are explored. Both models follow the mention-ranking approach while forming clusters of mentions. The first model uses a set of hand-crafted features whereas the second coreference model relies on embeddings learned from large-scale pre-trained language models for capturing similarities between a mention and its candidate antecedents. Several language models trained specifically for Turkish are used to obtain mention representations and their effectiveness is compared in conducted experiments using automatic metrics. We argue that the results of this study shed light on the possible contributions of neural architectures to Turkish coreference resolution.119683

Hypernode Graphs for Spectral Learning on Binary Relations over Sets

Paper accepted for publication at ECML/PKDD 2014International audienceWe introduce hypernode graphs as weighted binary relations between sets of nodes: a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non negative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend spectral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher order relations whereas it is not true for hypergraphs as shown in~\cite{Agarwal2006}. In order to prove the potential of the model, we represent multiple players games with hypernode graphs and introduce a novel method to infer skill ratings from game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill

Hypernode Graphs for Learning from Binary Relations between Groups in Networks

The aim of this paper is to propose methods for learning from interactions between groups in networks. We propose a proper extension of graphs, called hypernode graphs as a formal tool able to model group interactions. A hypernode graph is a collection of weighted relations between two disjoint groups of nodes. Weights quantify the individual participation of nodes to a given relation. We define Laplacians and kernels for hypernode graphs and prove that they strictly generalize over graph kernels and hypergraph kernels. We then proceed to prove that hypernode graphs correspond to signed graphs such that the matrix D − W is positive semi-definite. As a consequence, homophilic relations between groups may lead to non homophilic relations between individuals. We also define the notion of connected hypernode graphs and a resistance distance for connected hypernode graphs. Then, we propose spectral learning algorithms on hypernode graphs allowing to infer node ratings or node labelings. As a proof of concept, we model multiple players games with hypernode graphs and we define skill rating algorithms competitive with specialized algorithms

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

HYDRA: Large-scale Social Identity Linkage via Heterogeneous Behavior Modeling

We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID linkage information and the crossplatform structure consistency maximization. Extensive experiments on 10 million users across seven popular social network platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.? 2013 ACM.EI

Structured learning from heterogeneous behavior for social identity linkage

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ