Towards interoperable discourse annotation: discourse features in the Ontologies of Linguistic Annotation

This paper describes the extension of the Ontologies of Linguistic Annotation (OLiA) with respect to discourse features. The OLiA ontologies provide a a terminology repository that can be employed to facilitate the conceptual (semantic) interoperability of annotations of discourse phenomena as found in the most important corpora available to the community, including OntoNotes, the RST Discourse Treebank and the Penn Discourse Treebank. Along with selected schemes for information structure and coreference, discourse relations are discussed with special emphasis on the Penn Discourse Treebank and the RST Discourse Treebank. For an example contained in the intersection of both corpora, I show how ontologies can be employed to generalize over divergent annotation schemes

Vagueness and referential ambiguity in a large-scale annotated corpus

In this paper, we argue that difficulties in the definition of coreference itself contribute to lower inter-annotator agreement in certain cases. Data from a large referentially annotated corpus serves to corroborate this point, using a quantitative investigation to assess which effects or problems are likely to be the most prominent. Several examples where such problems occur are discussed in more detail, and we then propose a generalisation of Poesio, Reyle and Stevenson’s Justified Sloppiness Hypothesis to provide a unified model for these cases of disagreement and argue that a deeper understanding of the phenomena involved allows to tackle problematic cases in a more principled fashion than would be possible using only pre-theoretic intuitions

Extended nominal coreference and bridging anaphora (an approach to annotation of Czech data in Prague dependency treebank)

V této práci představujeme jeden z možných modelů zpracovaní rozšířené textové koreference a asociační anafory na velkém korpusu textů, který dále používáme pro anotaci daných vztahů na textech Pražského závislostního korpusu. Na základě literatury z oblastí teorie reference, diskurzu a některých dalších poznatků teoretické lingvistiky na jedné straně a s použitím existujících anotačních metodik na straně druhé jsme vytvořili detailní klasifikaci textově koreferenčních vztahů a typů vztahů asociační anafory. V rámci textové koreference rozlišujeme dva typy textově koreferenčních vztahů - koreferenční vztah mezi jmennými frázemi se specifickou referencí a koreferenční vztah mezi jmennými frázemi s nespecifickou, především generickou referencí. Pro asociační anaforu jsme stanovili šest typů vztahů: vztah PART mezi částí a celkem, vztah SUBSET mezi množinou a podmnožinou/prvkem množiny, vztah FUNCT mezi entitou a unikátní funkcí na této entitě, vztah CONTRAST sémantického a kontextového protikladu, vztah ANAF anaforického odkazování mezi nekoreferenčními entitami a vztah REST pro jiné případy asociační anafory. Jedním z úkolů výzkumu bylo vytvořit systém teoretických principů, které je nutno dodržovat při anotaci koreferenčních vztahů a asociační anafory. V rámci tohoto systému byl zaveden například princip...The dissertation presents one of the possible models of processmg extended textual coreference and bridging anaphora in a large textual corpora, which we then use for annotation of certain relations in texts of the Prague Oependency Treebank (POT). Based, on the one hand, on the literature concerning the theory of reference, discource and some findings of theoretical linguistics, and, on the other hand, using the existing methodology of annotations, we created a detailed classification of textual coreferential relations and types of bridging anaphora. Within textual coreference, we distinguish between two types of textual coreferential relations - coreferential relations between noun phrases with specific reference and coreferential relation between noun phrases with non-specific, primarily generic, reference. We determined six types of relations for bridging anaphora: relation PART- between part and whole; relation SUBSET - between a set and a subset or element of a set; FUNCT - between an object and a unique function on that entity; CONTRAST- between semantíc and contextual opposites; relation ANAF of anaphorical referencing between noncoreferencial objects; REST- for other examples of bridging anaphora. One of the goals of the research is to create a system of theoretical principals that would be used...Institute of Czech Language and Theory of CommunicationÚstav českého jazyka a teorie komunikaceFilozofická fakultaFaculty of Art

'Healthy' Coreference: Applying Coreference Resolution to the Health Education Domain

This thesis investigates coreference and its resolution within the domain of health education. Coreference is the relationship between two linguistic expressions that refer to the same real-world entity, and resolution involves identifying this relationship among sets of referring expressions. The coreference resolution task is considered among the most difficult of problems in Artificial Intelligence; in some cases, resolution is impossible even for humans. For example, "she" in the sentence "Lynn called Jennifer while she was on vacation" is genuinely ambiguous: the vacationer could be either Lynn or Jennifer. There are three primary motivations for this thesis. The first is that health education has never before been studied in this context. So far, the vast majority of coreference research has focused on news. Secondly, achieving domain-independent resolution is unlikely without understanding the extent to which coreference varies across different genres. Finally, coreference pervades language and is an essential part of coherent discourse. Its effective use is a key component of easy-to-understand health education materials, where readability is paramount. No suitable corpus of health education materials existed, so our first step was to create one. The comprehensive analysis of this corpus, which required manual annotation of coreference, confirmed our hypothesis that the coreference used in health education differs substantially from that in previously studied domains. This analysis was then used to shape the design of a knowledge-lean algorithm for resolving coreference. This algorithm performed surprisingly well on this corpus, e.g., successfully resolving over 85% of all pronouns when evaluated on unseen data. Despite the importance of coreferentially annotated corpora, only a handful are known to exist, likely because of the difficulty and cost of reliably annotating coreference. The paucity of genres represented in these existing annotated corpora creates an implicit bias in domain-independent coreference resolution. In an effort to address these issues, we plan to make our health education corpus available to the wider research community, hopefully encouraging a broader focus in the future

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

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

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

Making the Most of Crowd Information: Learning and Evaluation in AI tasks with Disagreements.

PhD ThesesThere is plenty of evidence that humans disagree on the interpretation of many tasks in Natural Language Processing (nlp) and Computer Vision (cv), from objective tasks rooted in linguistics such as part-of-speech tagging to more subjective (observerdependent) tasks such as classifying an image or deciding whether a proposition follows from a certain premise. While most learning in Artificial Intelligence (ai) still relies on the assumption that a single interpretation, captured by the gold label, exists for each item, a growing research body in recent years has focused on learning methods that do not rely on this assumption. Rather, they aim to learn ranges of truth amidst disagreement. This PhD research makes a contribution to this field of study. Firstly, we analytically review the evidence for disagreement on nlp and cv tasks, focusing on tasks where substantial datasets with such information have been created. As part of this review, we also discuss the most popular approaches to training models from datasets containing multiple judgments and group these methods together according to their handling of disagreement. Secondly, we make three proposals for learning with disagreement; soft-loss, multi-task learning from gold and crowds, and automatic temperature-scaled soft-loss. Thirdly, we address one gap in this field of study – the prevalence of hard metrics for model evaluation even when the gold assumption is shown to be an idealization – by proposing several previously existing metrics and novel soft metrics that do not make this assumption and analyzing the merits and assumptions of all the metrics, hard and soft. Finally, we carry out a systematic investigation of the key proposals in learning with disagreement by training them across several tasks, considering several ways to evaluate the resulting models and assessing the conditions under which each approach is effective. This is a key contribution of this research as research in learning with disagreement do not often test proposals across tasks, compare proposals with a variety of approaches, or evaluate using both soft metrics and hard metrics. The results obtained suggest, first of all, that it is essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials