Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

A Dutch coreference resolution system with an evaluation on literary fiction

Coreference resolution is the task of identifying descriptions that refer to the same entity. In this paper we consider the task of entity coreference resolution for Dutch with a particular focus on literary texts. We make three main contributions. First, we propose a simplified annotation scheme to reduce annotation effort. This scheme is used for the annotation of a corpus of 107k tokens from 21 contemporary works of literature. Second, we present a rule-based coreference resolution system for Dutch based on the Stanford deterministic multi-sieve coreference architecture and heuristic rules for quote attribution. Our system (dutchcoref) forms a simple but strong baseline and improves on previous systems in shared task evaluations. Finally, we perform an evaluation and error analysis on literary texts which highlights difficult cases of coreference in general, and the literary domain in particular. The code of our system is made available at https://github.com/andreasvc/dutchcoref

A Dutch coreference resolution system with an evaluation on literary fiction

Coreference resolution is the task of identifying descriptions that refer to the same entity. In this paper we consider the task of entity coreference resolution for Dutch with a particular focus on literary texts. We make three main contributions. First, we propose a simplified annotation scheme to reduce annotation effort. This scheme is used for the annotation of a corpus of 107k tokens from 21 contemporary works of literature. Second, we present a rule-based coreference resolution system for Dutch based on the Stanford deterministic multi-sieve coreference architecture and heuristic rules for quote attribution. Our system (dutchcoref) forms a simple but strong baseline and improves on previous systems in shared task evaluations. Finally, we perform an evaluation and error analysis on literary texts which highlights difficult cases of coreference in general, and the literary domain in particular. The code of our system is made available at https://github.com/andreasvc/dutchcoref

Coreference Resolution for Arabic

Recently, there has been enormous progress in coreference resolution. These recent developments were applied to Chinese, English and other languages, with outstanding results. However, languages with a rich morphology or fewer resources, such as Arabic, have not received as much attention. In fact, when this PhD work started there was no neural coreference resolver for Arabic, and we were not aware of any learning-based coreference resolver for Arabic since [Björkelund and Kuhn, 2014]. In addition, as far as we know, whereas lots of attention had been devoted to the phemomenon of zero anaphora in languages such as Chinese or Japanese, no neural model for Arabic zero-pronoun anaphora had been developed. In this thesis, we report on a series of experiments on Arabic coreference resolution in general and on zero anaphora in particular. We propose a new neural coreference resolver for Arabic, and we present a series of models for identifying and resolving Arabic zero pronouns. Our approach for zero-pronoun identification and resolution is applicable to other languages, and was also evaluated on Chinese, with results surpassing the state of the art at the time. This research also involved producing revised versions of standard datasets for Arabic coreference

Korreferentzia-ebazpena euskarazko testuetan.

203 p.Gaur egun, korreferentzia-ebazpen automatikoa gakotzat har dezakegu testuak ulertuahal izateko; ondorioz, behar-beharrezkoa da diskurtsoaren ulerkuntza sakona eskatzenduten Lengoaia Naturalaren Prozesamenduko (NLP) hainbat atazatan.Testu bateko bi espresio testualek objektu berbera adierazi edo erreferentziatzendutenean, bi espresio horien artean korreferentzia-erlazio bat dagoela esan ohi da. Testubatean ager daitezkeen espresio testual horien arteko korreferentzia-erlazioak ebazteahelburu duen atazari korreferentzia-ebazpena deritzo.Tesi-lan hau, hizkuntzalaritza konputazionalaren arloan kokatzen da eta euskarazidatzitako testuen korreferentzia-ebazpen automatikoa du helburu, zehazkiago esanda,euskarazko korreferentzia-ebazpen automatikoa gauzatzeko dagoen baliabide eta tresnenhutsunea betetzea du helburu.Tesi-lan honetan, lehenik euskarazko testuetan ager daitezkeen espresio testualakautomatikoki identifikatzeko garatu dugun erregelatan oinarritutako tresna azaltzen da.Ondoren, Stanfordeko unibertsitatean ingeleserako diseinatu den erregelatanoinarritutako korreferentzia-ebazpenerako sistema euskararen ezaugarrietara nolaegokitu den eta ezagutza-base semantikoak erabiliz nola hobetu dugun aurkezten da.Bukatzeko, ikasketa automatikoan oinarritzen den BART korreferentzia-ebazpenerakosistema euskarara egokitzeko eta hobetzeko egindako lana azaltzen da

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

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Gamifying Language Resource Acquisition

PhD ThesisNatural Language Processing, is an important collection of methods for processing the vast amounts of available natural language text we continually produce. These methods make use of supervised learning, an approach that learns from large amounts of annotated data. As humans, we’re able to provide information about text that such systems can learn from. Historically, this was carried out by small groups of experts. However, this did not scale. This led to various crowdsourcing approaches being taken that used large pools of non-experts. The traditional form of crowdsourcing was to pay users small amounts of money to complete tasks. As time progressed, gamification approaches such as GWAPs, showed various benefits over the micro-payment methods used before. These included a cost saving, worker training opportunities, increased worker engagement and potential to far exceed the scale of crowdsourcing. While these were successful in domains such as image labelling, they struggled in the domain of text annotation, which wasn’t such a natural fit. Despite many challenges, there were also clearly many opportunities and benefits to applying this approach to text annotation. Many of these are demonstrated by Phrase Detectives. Based on lessons learned from Phrase Detectives and investigations into other GWAPs, in this work, we attempt to create full GWAPs for NLP, extracting the benefits of the methodology. This includes training, high quality output from non-experts and a truly game-like GWAP design that players are happy to play voluntarily