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Academic literature on the topic 'Coreference'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2025

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Journal articles on the topic "Coreference"

1

Gilbert, Nathan. "Acquiring Domain Specific Knowledge and Coreference Cues for Coreference Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 2390–91. http://dx.doi.org/10.1609/aaai.v26i1.8190.

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Current Coreference Resolution systems utilize a broad range of general knowledge features to make resolutions in a general setting. These approaches ignore coreference knowledge found in domain specific collections and how coreferent entities interact in different domains. This research addresses these issues by developing knowledge bases of coreference characteristics drawn from annotated and unannotated domain texts and utilizing lexical and discourse information to improve resolution.

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Žitkus, Voldemaras, Rita Butkienė, Rimantas Butleris, Rytis Maskeliūnas, Robertas Damaševičius, and Marcin Woźniak. "Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records." Computational and Mathematical Methods in Medicine 2019 (March 20, 2019): 1–14. http://dx.doi.org/10.1155/2019/9079840.

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Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

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Lutfullina, G. F. "TEMPORAL COREFERENCE IN ENGLISH." Voprosy Kognitivnoy Lingvistiki, no. 2 (2024): 99–109. http://dx.doi.org/10.20916/1812-3228-2024-2-99-109.

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The article using the material of the English language focuses on studying the issues of temporal coreference. The purpose of the study is to identify the possibilities of implementing temporal coreference using Progressive forms in the English language. In verbal representing temporal reference in English, the referential moment can be expressed by Past Simple, implying a limited interval. These Tense forms may be antecedents for others. The remaining Tenses are anaphoric to it. In case of non-verbal representation of temporal reference, the adverbial modifier of pointed temporal semantics can express the referential moment. Coreference is possible in case of: 1) two non-verbally expressed time intervals, referring to one time interval: the first specifies a larger period, including the second; 2) two verbally expressed intervals; 3) one verbally expressed interval and a second, non-verbally represented. Temporal coreference is represented where a non-verbally expressed referential moment is anaphoric with a time interval verbally implied by predicates in the following Tenses: Past Progressive, Past Simple, Present Progressive, Future Simple. Present Progressive is characterized by coreference with the moment of speech or the present actual moment. Two predicates in Past Progressive and Past Simple can be coreferent to the referential moment represented by the predicate in the Past Simple. Two predicates in Present and Past Progressive can be coreferent provided they are coreferent to the moment of speech or to another moment.

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Darchuk, Natalia. "Coreference in the poetic works of Lesia Ukrainka." Ukrainian Linguistics, no. 52 (2022): 93–115. http://dx.doi.org/10.17721/um/52(2022).93-115.

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The category of coreference plays an important role in the creation of texts, as it is based on fundamental principles of text organization. The linearity of a text and the non-linearity of a situation described in the text are closely related to the mention of elements of a situation with another nomination of this object. Therefore, chains of coreferential names appear in the text and, having a binary relation, partly cause the emergence of a secondary nomination, which is an anaphoric relation with the primary nomination. The purpose of this article is to study the category of coreference in the poetic speech of Lesia Ukrainka based on the corpus of the Ukrainian language represented on mova.info. The object of the research is the idiostyle of Lesia Ukrainka’s poetry, and the subject is the functioning of the category of coreference. The categorical essence of coreference is characterized, the types of representatives of real objects, their functional and stylistic varieties and typical objects of representation in a poetic text are determined, and the most frequent structures are considered. Software for working with coreference has been developed and implemented. It has a user-friendly interface, which allows searching, sort, and doing the quantitative processing of the collected information according to the needs of researchers. On the material of 153 texts, 1520 referentially identical pairs are established. The coreferentiality index and the index of coreference coverage of the text proposed in this article helped to quantitatively assess the saturation of the text with the category of coreference. In addition, the classification of coreferential relations is composed: 1) identity; 2) collection gap; 3) part-whole; 4) predicative identity; 5) metonymy; 6) association. A dependency tree of a sentence made it possible to determine the location of coreferential pairs in the sentence, the position relative to each other, the belonging of the segment with the coreferent or the referent to the complicators of the sentence, i.e., a subordinate clause or phrases. This representation also helped to establish the type of syntactic relationship between the coreferent and the referent, the type of syntactic-semantic relationship between them, the presence of referent-dependent words, the intersection of the coreferent and the referent in gender and number. Automatic extraction of coreferential pairs from dependency trees allowed building intersentential coreference.

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Rahman, A., and V. Ng. "Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution." Journal of Artificial Intelligence Research 40 (February 25, 2011): 469–521. http://dx.doi.org/10.1613/jair.3120.

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Traditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the pre-statistical NLP era in terms of sophistication. Two independent lines of recent research have attempted to improve the mention-pair model, one by acquiring the mention-ranking model to rank preceding mentions for a given anaphor, and the other by training the entity-mention model to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution, which combines the strengths of the mention-ranking model and the entity-mention model, and is therefore theoretically more appealing than both of these models. In addition, we seek to improve cluster rankers via two extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity by jointly modeling anaphoricity determination and coreference resolution. Experimental results on the ACE data sets demonstrate the superior performance of cluster rankers to competing approaches as well as the effectiveness of our two extensions.

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De Marneffe, Marie-Catherine, Marta Recasens, and Christopher Potts. "Modeling the Lifespan of Discourse Entities with Application to Coreference Resolution." Journal of Artificial Intelligence Research 52 (March 31, 2015): 445–75. http://dx.doi.org/10.1613/jair.4565.

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A discourse typically involves numerous entities, but few are mentioned more than once. Distinguishing those that die out after just one mention (singleton) from those that lead longer lives (coreferent) would dramatically simplify the hypothesis space for coreference resolution models, leading to increased performance. To realize these gains, we build a classifier for predicting the singleton/coreferent distinction. The models feature representations synthesize linguistic insights about the factors affecting discourse entity lifespans (especially negation, modality, and attitude predication) with existing results about the benefits of surface (part-of-speech and n-gram-based) features for coreference resolution. The model is effective in its own right, and the feature representations help to identify the anchor phrases in bridging anaphora as well. Furthermore, incorporating the model into two very different state-of-the-art coreference resolution systems, one rule-based and the other learning-based, yields significant performance improvements.

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Balaji, J., T. V. Geetha, and P. Ranjani. "Graph-Based Bootstrapping for Coreference Resolution." Journal of Intelligent Systems 23, no. 3 (September 1, 2014): 293–310. http://dx.doi.org/10.1515/jisys-2013-0056.

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AbstractCoreference resolution is a challenging natural language processing task, and it is difficult to identify the correct mentions of an entity that can be any noun or noun phrase. In this article, a semisupervised, two-stage pattern-based bootstrapping approach is proposed for the coreference resolution task. During Stage 1, the possible mentions are identified using word-based features, and during Stage 2, the correct mentions are identified by filtering the non-coreferents of an entity using statistical measures and graph-based features. Whereas the existing approaches use morphosyntactic and number/gender agreement features, the proposed approach uses semantic graph-based context-level semantics and nested noun phrases in the correct mentions identification. Moreover, mentions without the number/gender information are identified, using the context-based features of the semantic graph. The evaluation performed for the coreference resolution shows significant improvements, when compared with the word association-based bootstrapping systems.

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POGORILYY, S. D., and P. V. BILETSKY. "USAGE OF A GRAPHICS PROCESSOR TO ACCELERATE COREFERENCE RESOLUTION WHILE USING THE RoBERTa MODEL." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 2 - №1, no. 33-34 (2022): 4–9. http://dx.doi.org/10.31474/1996-1588-2021-2-33-4-9.

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"The problem of finding coreferential objects (coreference resolution) in Ukrainian-language texts is considered, examples of co-referentiality in the Ukrainian language are given. Coreference in texts means the relationship between syntactic units that point to the same object (referent) in a given context. The differences between English-language and Ukrainian-language texts in solving the problem of searching for coreferenced objects and the difficulties that arise in finding coreferenced objects are noted. In particular, there are differences in the order of words in sentences: strict word order in English and arbitrary word order in Ukrainian. It is also pointed out the need to take into account the relationship between objects that are not part of the co-reference groups (predicates and secondary parts of sentence). Created an application that searches for reference objects in Ukrainian-language texts using the Transformers library and the RoBERTa language model. The use of a graphics processor to speed up the search for referential objects by the algorithm is considered. The work of the algorithm was tested on a set containing 2500 texts. 21.3 times acceleration reached due to the use of a graphics processor (GPU) in comparison to the performance of CPU. The resulting acceleration has greatly facilitated the development and testing of the application."

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Lu, Jing, and Vincent Ng. "Span-Based Event Coreference Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (May 18, 2021): 13489–97. http://dx.doi.org/10.1609/aaai.v35i15.17591.

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Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whether exploiting the dependency between event coreference and the related subtask of trigger detection; and (3) whether automatically computed entity coreference information can benefit span-based event coreference resolution. Empirical results on the standard evaluation dataset provide affirmative answers to all three questions.

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RECASENS, M., and E. HOVY. "BLANC: Implementing the Rand index for coreference evaluation." Natural Language Engineering 17, no. 4 (December 6, 2010): 485–510. http://dx.doi.org/10.1017/s135132491000029x.

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AbstractThis paper addresses the current state of coreference resolution evaluation, in which different measures (notably, MUC, B3, CEAF, and ACE-value) are applied in different studies. None of them is fully adequate, and their measures are not commensurate. We enumerate the desiderata for a coreference scoring measure, discuss the strong and weak points of the existing measures, and propose the BiLateral Assessment of Noun-Phrase Coreference, a variation of the Rand index created to suit the coreference task. The BiLateral Assessment of Noun-Phrase Coreference rewards both coreference and non-coreference links by averaging the F-scores of the two types, does not ignore singletons – the main problem with the MUC score – and does not inflate the score in their presence – a problem with the B3 and CEAF scores. In addition, its fine granularity is consistent over the whole range of scores and affords better discrimination between systems.

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Dissertations / Theses on the topic "Coreference"

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Nicol, Janet Lee. "Coreference processing during sentence comprehension." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/14421.

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Yoon, Chulmin. "Essays on De Jure Coreference." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595589314146257.

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HERNANDEZ, ADRIEL GARCIA. "COREFERENCE RESOLUTION FOR THE ENGLISH LANGUAGE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30730@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
PROGRAMA DE EXCELENCIA ACADEMICA
BOLSA NOTA 10
Um dos problemas encontrados nos sistemas de processamento de linguagem natural é a dificuldade em identificar elementos textuais que se referem à mesma entidade. Este fenômeno é chamado de correferência. Resolver esse problema é parte integrante da compreensão do discurso, permitindo que os usuários da linguagem conectem as partes da informação de fala relativas à mesma entidade. Por conseguinte, a resolução de correferência é um importante foco de atenção no processamento da linguagem natural.Apesar da riqueza das pesquisas existentes, o desempenho atual dos sistemas de resolução de correferência ainda não atingiu um nível satisfatório. Neste trabalho, descrevemos um sistema de aprendizado estruturado para resolução de correferências em restrições que explora duas técnicas: árvores de correferência latente e indução automática de atributos guiadas por entropia. A modelagem de árvore latente torna o problema de aprendizagem computacionalmente viável porque incorpora uma estrutura escondida relevante. Além disso, utilizando um método automático de indução de recursos, podemos construir eficientemente modelos não-lineares, usando algoritmos de aprendizado de modelo linear como, por exemplo, o algoritmo de perceptron estruturado e esparso.Nós avaliamos o sistema para textos em inglês, utilizando o conjunto de dados da CoNLL-2012 Shared Task. Para a língua inglesa, nosso sistema obteve um valor de 62.24 por cento no score oficial dessa competição. Este resultado está abaixo do desempenho no estado da arte para esta tarefa que é de 65.73 por cento. No entanto, nossa solução reduz significativamente o tempo de obtenção dos clusters dos documentos, pois, nosso sistema leva 0.35 segundos por documento no conjunto de testes, enquanto no estado da arte, leva 5 segundos para cada um.
One of the problems found in natural language processing systems, is the difficulty to identify textual elements referring to the same entity, this task is called coreference. Solving this problem is an integral part of discourse comprehension since it allows language users to connect the pieces of speech information concerning to the same entity. Consequently, coreference resolution is a key task in natural language processing.Despite the large efforts of existing research, the current performance of coreference resolution systems has not reached a satisfactory level yet. In this work, we describe a structure learning system for unrestricted coreferencere solution that explores two techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible,since it incorporates are levant hidden structure. Additionally,using an automatic feature induction method, we can efciently build enhanced non-linear models using linear model learning algorithms, namely, the structure dandsparse perceptron algorithm. We evaluate the system on the CoNLL-2012 Shared Task closed track data set, for the English portion. The proposed system obtains a 62.24 per cent value on the competition s official score. This result is be low the 65.73 per cent, the state-of-the-art performance for this task. Nevertheless, our solution significantly reduces the time to obtain the clusters of adocument, since, our system takes 0.35 seconds per document in the testing set, while in the state-of-the-art, it takes 5 seconds for each one.

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WERNER, ENEIDA FIGUEIRA DE ALMEIDA. "REVISION IN WRITING AND COREFERENCE ISSUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36163@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
O objetivo desta tese é investigar o processo de revisão da escrita e o processo de estabelecimento da correferência quanto à forma como são monitorados por grupos com diferentes graus de experiência em escrita. A pesquisa insere-se no quadro dos estudos sobre processamento da escrita, focalizando o processo da produção, e ancora-se, teoricamente, no tocante à pesquisa em escrita, no modelo de processamento cognitivo da escrita de Flower e Hayes (1980) e no modelo de revisão de Hayes (1987). Nos estudos da correferência, consideram-se as principais teorias voltadas para a investigação da influência de fatores que favorecem a acessibilidade à memória para seu estabelecimento, a Teoria da Acessibilidade (Ariel, 1990), a Teoria da Centralização (Grosz, Joshi e Weinstein, 1995) e a Hipótese da Carga Informacional (Almor, 1999). Relacionamos as questões teóricas aos dados de natureza cognitiva obtidos por meio de metodologia experimental. O laboratório utilizado foi o LAPAL, na PUC-Rio. Os experimentos conduzidos basearam-se em tarefas de produção e revisão de textos. Foi utilizada a ferramenta de keystroke logging Inputlog (http://www.inputlog.net/) para gravação e análise dos dados. Os participantes eram alunos de graduação e de pós-graduação de uma instituição pública e de uma instituição privada no Rio de Janeiro. No primeiro experimento foram analisados dados de natureza global do processamento da escrita e do processamento da correferência a partir de imagens-estímulos de duas histórias em quadrinhos, sem material verbal. No que tange ao comportamento global do processamento de escrita, foram verificadas medidas relativas ao processo e ao produto do texto produzido (em termos de número de caracteres e de palavras) e também relativas a pausas e tipos de revisões realizadas. No âmbito das medidas voltadas especificamente para o processamento da correferência, foramanalisados dados relacionados aos tipos de expressões referenciais selecionadas para introduzir e retomar entidades discursivas, bem como quanto ao momento em que elementos de retomada foram revistos (revisão do tipo imediata ou posterior) e à natureza do tipo de alteração implementada no que tange ao grau de especificidade do termo usado na substituição (mais/menos específico). O segundo experimento objetivou investigar os fatores que influenciam a escolha de uma expressão referencial anafórica a partir da informação contida no antecedente. Foi conduzida tarefa de revisão com quatro textos de mesmo tipo narrativo. Em cada tipo de texto avaliou-se os tipos de retomadas anafóricas das expressões referenciais em função do grau de ativação de informação na memória favorecido pela acessibilidade ao antecedente. Foram tomadas como variáveis independentes a função sintática do antecedente (mais suj.; menos suj.), o papel temático (mais agente; menos agente), e a distância entre o antecedente e o elemento de retomada (igual período; diferente período). No primeiro experimento os resultados apontaram divergências entre os tipos de revisões efetuadas (imediatas/posteriores) e quanto à proporção de revisões efetuadas (apagamentos/inserções) indicando que o grupo de alunos de pós-graduação empregou mais qualitativamente estratégias e recursos de revisão no monitoramento de seus textos do que os alunos de graduação. No segundo experimento, na análise estatística conduzida para cada grupo separadamente, foi verificado efeito principal de posição sintática (nos 2 grupos), distância (nos 2 grupos), e papel temático (no grupo de pós-graduação). Além disso, verificaram-se efeitos de interação entre posição e distância, e entre posição, papel temático e distância (grupo de graduação) e de posição e distância (grupo de pós-graduação). A qualidade das revisões efetuadas foi diferente, tendo o grupo de alunos de pós-graduação efetuado mais revisões do tipo posterior e percentualmente mais revisões que implicaram modificações na qualidade textual. Em conjunt
The purpose of this doctoral thesis is to investigate the writing process and the process of establishing coreference as to how they are monitored by groups of different degrees of writing experience. The research is part of the study of writing processing, focusing on the production process, and is theoretically anchored in writing research related to the Cognitive Writing Model of Hayes and Flower (1980) and in Hayes s Writing Revision Model (1987). In the studies of coreference, we consider the main theories that investigate the influence of factors that favour accessibility to memory, Accessibility Theory (Ariel, 1990), the Centering Theory (Grosz, Joshi and Weinstein, 1995) and the Information Load Hypothesis (Almor, 1999). We related the theoretical questions to the data captured by means of experimental methodology. The laboratory used was LAPAL, at PUC-Rio. The experiments conducted were based on writing production and revision tasks and we used the technological tool of keystroke logging Inputlog (http://www.inputlog.net/) to record and analyse data. Participants were graduate and post graduate students of public and private institutions in Rio de Janeiro.In the first experiment the data analysed related to production of writing and coreference processing from image-stimuli of two comic strips without verbal material. Concerning the measures related to writing production, we analysed the relation between the process and product in terms of the number of characters and words as well as pauses and the types of revisions made. Regarding the measures of coreference processing, we examined the types of of referential expressions selected to introduce and to establish coreference within discourse entities, as well as data related to the moment when correferential elements were revised (immediate or delayed revisions) and the degree of specificity implied in the alterations worked out. The second experiment aimed to investigate the factors that influence the choice of anaphoric referential expressions from the type of information contained in the antecedent. We conducted an experiment of writing revision consisting of four different texts of the same discursive genre. In each of them we took into account the degree of activation in memory provided by information that favours accessibility to memory stored items. The independent variables were the syntactic function of the antecedent(more subject/less subject), the thematic role of the antecedent (more agent/less agent) and the the distance between the antecedent and the anaphoric referential expression (equal period/different period). Results from the first experiment pointed out differences between the types of revisions (immediate/delayed) and the proportion of revisions made (deletions/insertions) indicating that post-graduate group used more revision strategy resources while monitoring their production as compared to the group of graduates. In the second experiment, statistical analysis conducted for each group separately revealed effects of the factors considered as for syntactic position (in the 2 groups), thematic role (in the post-graduates group) and distance (in both groups). In addition, interaction effects between distance and syntactic position and between position, thematic role and distance (graduates group) and position and distance (post-graduates group) were significant. The quality of the revisions was proven diverse, having post-graduates proceeded to more delayed revisions that imply alteration in overall text quality than the group of graduates. As a whole, the experiments conducted allowed us to identify differences between the experimental groups and suggest evidence that schooling level plays an important role in writing and in the choices made in for coreference processing.

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Bodnari, Andreea. "Joint multilingual learning for coreference resolution." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91126.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
98
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 112-120).
Natural language is a pervasive human skill not yet fully achievable by automated computing systems. The main challenge is understanding how to computationally model both the depth and the breadth of natural languages. In this thesis, I present two probabilistic models that systematically model both the depth and the breadth of natural languages for two different linguistic tasks: syntactic parsing and joint learning of named entity recognition and coreference resolution. The syntactic parsing model outperforms current state-of-the-art models by discovering linguistic information shared across languages at the granular level of a sentence. The coreference resolution system is one of the first attempts at joint multilingual modeling of named entity recognition and coreference resolution with limited linguistic resources. It performs second best on three out of four languages when compared to state-of-the-art systems built with rich linguistic resources. I show that we can simultaneously model both the depth and the breadth of natural languages using the underlying linguistic structure shared across languages.
by Andreea Bodnari.
Ph. D.

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Webster, Kellie. "Improved Coreference Resolution Using Cognitive Insights." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/15468.

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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.

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Corazza, Michele. "Coreference Resoultion basata su reti neurali deep." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14554/.

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L’utilizzo di reti neurali deep nell’ambito dell’elaborazione del linguaggio naturale sta conducendo negli ultimi anni a risultati significativi in task molto disparati, dalla speech recognition all’analisi semantica. La ragione di tali innovazioni risiede nelle capacità computazionali odierne, in grado di supportare l’utilizzo di reti neurali con molti livelli nascosti, dette appunto deep, e di strumenti innovativi quali le recurrent neural network, convolutional neural network e la possibilità di costruire word embedding tramite word2vec o strumenti analoghi. Fra i task irrisolti nell’ambito delle reti neurali, è di particolare interesse lo studio della coreference resolution. In tale task l’obiettivo è quello di risolvere le coreferenze in un testo, ovvero associare menzioni che si riferiscono ad una stessa entità. Il fenomeno in esame risulta particolarmente interessante, in quanto comprende aspetti semantici e sintattici del linguaggio, che devono essere utilizzati per giungere a buoni risultati. Un ulteriore caratteristica della coreference è la relazione di tale fenomeno con il concetto di “contesto linguistico”. È infatti dal contesto che circonda una menzione che è possibile intuire a quale entità esso si riferisca. Si presenta con questa tesi un solver per la coreference basato su reti neurali deep, che sfrutti reti recurrent per trattare il problema. La proposta si basa sulla supposizione che sia necessario introdurre delle componenti della rete che siano in grado di fornire una rappresentazione delle menzioni, in modo da poter utilizzare tali risultati per affrontare il problema della coreference resolution.

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Nilsson, Kristina. "Hybrid Methods for Coreference Resolution in Swedish." Doctoral thesis, Stockholm : Department of Linguistics, Stockholm University, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-38395.

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Christiansen, Thomas Wulstan. "Coreference and noun phrase selection in Italian." Thesis, University of Salford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365982.

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Kunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.

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Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU). In this thesis, we implement and explore two recent models that include the concept of coreference in Recurrent Neural Network (RNN)-based Language Models (LM). Entity and reference decisions are modeled explicitly in these models using attention mechanisms. Both models learn to save the previously observed entities in a set and to decide if the next token created by the LM is a mention of one of the entities in the set, an entity that has not been observed yet, or not an entity. After a theoretical analysis where we compare the two LMs to each other and to a state of the art Coreference Resolution system, we perform an extensive quantitative and qualitative analysis. For this purpose, we train the two models and a classical RNN-LM as the baseline model on the OntoNotes 5.0 corpus with coreference annotation. While we do not reach the baseline in the perplexity metric, we show that the models’ relative performance on entity tokens has the potential to improve when including the explicit entity modeling. We show that the most challenging point in the systems is the decision if the next token is an entity token, while the decision which entity the next token refers to performs comparatively well. Our analysis in the context of a text generation task shows that a wide-spread error source for the mention creation process is the confusion of tokens that refer to related but different entities in the real world, presumably a result of the context-based word representations in the models. Our re-implementation of the DeepMind model by Yang et al. 2016 performs notably better than the re-implementation of the EntityNLM model by Ji et al. 2017 with a perplexity of 107 compared to a perplexity of 131.

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Books on the topic "Coreference"

1

Eguren, Luis, and Olga Fernández-Soriano, eds. Coreference, Modality, and Focus. Amsterdam: John Benjamins Publishing Company, 2007. http://dx.doi.org/10.1075/la.111.

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Lappin, Shalom. Pronominal binding and coreference. Berlin: Walter de Gruyter, 1987.

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Ogrodniczuk, Maciej. Coreference: Annotation, resolution and evaluation in Polish. Berlin: De Gruyter, 2015.

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Luis, Eguren, and Fernández Soriano Olga, eds. Coreference, modality, and focus: Studies on the syntax-semantics interface. Amsterdam: J. Benjamins Pub. Co., 2007.

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Taber, John, and Kei Kataoka. Coreference and Qualification. Edited by Jonardon Ganeri. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199314621.013.19.

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The Buddhist thinker Dignāga justified his proposal that words refer to “exclusions” (apohas) in part as the only way two words could be used to refer to the same thing or qualify each other in expressions such as “existing pot” and “blue lotus.” Specifically, he argued that if words referred to real universals their meanings would block each other, preventing the words from being used in combination. The advantage of apohas, he believed, is that they are “insubstantial” and so do not resist being combined. Kumārila challenged Dignāga’s view by alleging that all of the problems that he saw for universals when it comes to coreference and qualification are problems for apohas as well. Dharmakīrti, then, defended Dignāga’s apoha theory against these attacks by emphasizing the conventional nature of meaning and the flexibility of words to convey whatever we want—whether properties in isolation or things possessing multiple properties.

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Žitkus, Voldemaras. Lithuanian Coreference Corpus. KTU, 2018. http://dx.doi.org/10.7220/20.500.12259/240255.

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Baauw, Sergio. The Acquisition of Binding and Coreference. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.22.

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In this chapter the acquisition of pronouns and reflexives is discussed. It reviews several accounts of the so-called Delay of Principle B Effect, the absence of this effect in some languages, and the structural factors that influence its appearance in child language. It also discusses children’s alledged target-like performance on reflexives in several languages with different type of reflexives. The chapter concludes that provided a balanced experimental design is used, the experimental results point at early mastery of Principle A and B, and that children’s difficulties with the interpretation of pronouns and reflexives are to be found at the interfaces between syntax and discourse or semantics, and may be due to limited (syntactic) processing resources.

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Toward a generative grammar of coreference. Cambridge, MA: Distributed by working papers in linguistics, MIT, Dept. of Linguistics, 2004.

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Coreference: Annotation, Resolution and Evaluation in Polish. De Gruyter, Inc., 2015.

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Zawislawska, Magdalena, Maciej Ogrodniczuk, Katarzyna Glowinska, Mateusz Kopec, and Agata Savary. Coreference: Annotation, Resolution and Evaluation in Polish. de Gruyter GmbH, Walter, 2014.

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Book chapters on the topic "Coreference"

1

Stede, Manfred. "Coreference Resolution." In Discourse Processing, 39–77. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02144-2_3.

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Ogrodniczuk, Maciej, Katarzyna Głowińska, Mateusz Kopeć, Agata Savary, and Magdalena Zawisławska. "Polish Coreference Corpus." In Human Language Technology. Challenges for Computer Science and Linguistics, 215–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43808-5_17.

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Dekker, Paul. "Coreference and Representationalism." In Reference and Anaphoric Relations, 287–310. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-3947-2_15.

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Steinberger, Josef, Mijail Kabadjov, and Massimo Poesio. "Coreference Applications to Summarization." In Anaphora Resolution, 433–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47909-4_15.

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Lasnik, Howard. "Remarks on Coreference 1976." In Studies in Natural Language and Linguistic Theory, 90–109. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2542-7_4.

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Hintikka, Jaakko, and Jack Kulas. "GTS Explains Coreference Restrictions." In Anaphora and Definite Descriptions, 157–96. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5410-6_7.

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García-Carpintero, Manuel. "Pretend Reference and Coreference." In Perspectives in Pragmatics, Philosophy & Psychology, 333–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21395-8_16.

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Harweg, Roland. "Subordinate and embedded coreference." In Text and Discourse Connectedness, 65. Amsterdam: John Benjamins Publishing Company, 1989. http://dx.doi.org/10.1075/slcs.16.09har.

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Stati, Sorin. "Isotopy, coreference, and redundancy." In Text and Discourse Connectedness, 207. Amsterdam: John Benjamins Publishing Company, 1989. http://dx.doi.org/10.1075/slcs.16.18sta.

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Gordon, Peter C., and Randall Hendrick. "Dimensions of Grammatical Coreference." In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, 424–29. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781315782416-83.

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Conference papers on the topic "Coreference"

1

Ashury Tahan, Shir, Amir David Nissan Cohen, Nadav Cohen, Yoram Louzoun, and Yoav Goldberg. "Data-driven Coreference-based Ontology Building." In Findings of the Association for Computational Linguistics: EMNLP 2024, 14290–300. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.834.

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Skachkova, Natalia. "Multilingual coreference resolution as text generation." In Proceedings of The Seventh Workshop on Computational Models of Reference, Anaphora and Coreference, 114–22. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.crac-1.11.

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Chistova, Elena, and Ivan Smirnov. "Light Coreference Resolution for Russian with Hierarchical Discourse Features." In INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-34-41.

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Coreference resolution is the task of identifying and grouping mentions referring to the same real-world entity. Previous neural models have mainly focused on learning span representations and pairwise scores for coreference decisions. However, current methods do not explicitly capture the referential choice in the hierarchical discourse, an important factor in coreference resolution. In this study, we propose a new approach that incorporates rhetorical information into neural coreference resolution models. We collect rhetorical featuresfrom automated discourse parses and examine their impact. As a base model, we implement an end-to-end span-based coreference resolver using a partially fine-tuned multilingual entity-aware language model LUKE. We evaluate our method on the RuCoCo-23 Shared Task for coreference resolution in Russian. Our best model employing rhetorical distance between mentions has ranked 1st on the development set (74.6% F1) and 2nd on the test set (73.3% F1) of the Shared Task¹. We hope that our work will inspire further research on incorporating discourse information in neural coreference resolution models.

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Wang, Liming, Shengyu Feng, Xudong Lin, Manling Li, Heng Ji, and Shih-Fu Chang. "Coreference by Appearance: Visually Grounded Event Coreference Resolution." In Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.crac-1.14.

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Dai, Zeyu, Hongliang Fei, and Ping Li. "Coreference Aware Representation Learning for Neural Named Entity Recognition." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/687.

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Recent neural network models have achieved state-of-the-art performance on the task of named entity recognition (NER). However, previous neural network models typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases or entities. In this paper, we propose a novel approach to learn coreference-aware word representations for the NER task at the document level. In particular, we enrich the well-known neural architecture ``CNN-BiLSTM-CRF'' with a coreference layer on top of the BiLSTM layer to incorporate coreferential relations. Furthermore, we introduce the coreference regularization to ensure the coreferential entities to share similar representations and consistent predictions within the same coreference cluster. Our proposed model achieves new state-of-the-art performance on two NER benchmarks: CoNLL-2003 and OntoNotes v5.0. More importantly, we demonstrate that our framework does not rely on gold coreference knowledge, and can still work well even when the coreferential relations are generated by a third-party toolkit.

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van Deemter, Kees, and Rodger Kibble. "What is coreference, and what should coreference annotation be?" In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1999. http://dx.doi.org/10.3115/1608810.1608828.

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Lu, Jing, and Vincent Ng. "Event Coreference Resolution: A Survey of Two Decades of Research." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/773.

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Recent years have seen a gradual shift of focus from entity-based tasks to event-based tasks in information extraction research. Being a core event-based task, event coreference resolution is less studied but arguably more challenging than entity coreference resolution. This paper provides an overview of the major milestones made in event coreference research since its inception two decades ago.

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Pamay, Tugba, and Gulsen Eryigit. "Turkish Coreference Resolution." In 2018 Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2018. http://dx.doi.org/10.1109/inista.2018.8466293.

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Harabagiu, Sanda M., and Steven J. Maiorano. "Multilingual coreference resolution." In the sixth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/974147.974167.

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Yin, Kayo, Kenneth DeHaan, and Malihe Alikhani. "Signed Coreference Resolution." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.405.

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Reports on the topic "Coreference"

1

Stoyanov, V., C. Cardie, N. Gilbert, E. Riloff, D. Buttler, and D. Hysom. Reconcile: A Coreference Resolution Research Platform. Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/970156.

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Gooi, Chung H., and James Allan. Cross-Document Coreference on a Large Scale Corpus. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada458579.

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Firpo, M. A Novel Approach to Semantic and Coreference Annotation at LLNL. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15014772.

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