Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.Comment: To appear in EMNLP 2019 (with corrected Fig. 2

Robustness in Coreference Resolution

Coreference resolution is the task of determining different expressions of a text that refer to the same entity. The resolution of coreferring expressions is an essential step for automatic interpretation of the text. While coreference information is beneficial for various NLP tasks like summarization, question answering, and information extraction, state-of-the-art coreference resolvers are barely used in any of these tasks. The problem is the lack of robustness in coreference resolution systems. A coreference resolver that gets higher scores on the standard evaluation set does not necessarily perform better than the others on a new test set. In this thesis, we introduce robustness in coreference resolution by (1) introducing a reliable evaluation framework for recognizing robust improvements, and (2) proposing a solution that results in robust coreference resolvers. As the first step of setting up the evaluation framework, we introduce a reliable evaluation metric, called LEA, that overcomes the drawbacks of the existing metrics. We analyze LEA based on various types of errors in coreference outputs and show that it results in reliable scores. In addition to an evaluation metric, we also introduce an evaluation setting in which we disentangle coreference evaluations from parsing complexities. Coreference resolution is affected by parsing complexities for detecting the boundaries of expressions that have complex syntactic structures. We reduce the effect of parsing errors in coreference evaluation by automatically extracting a minimum span for each expression. We then emphasize the importance of out-of-domain evaluations and generalization in coreference resolution and discuss the reasons behind the poor generalization of state-of-the-art coreference resolvers. Finally, we show that enhancing state-of-the-art coreference resolvers with linguistic features is a promising approach for making coreference resolvers robust across domains. The incorporation of linguistic features with all their values does not improve the performance. However, we introduce an efficient pattern mining approach, called EPM, that mines all feature-value combinations that are discriminative for coreference relations. We then only incorporate feature-values that are discriminative for coreference relations. By employing EPM feature-values, performance improves significantly across various domains

A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.Comment: Accepted to Proceedings of EMNLP 2020 (Findings

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Proposition-based summarization with a coherence-driven incremental model

Summarization models which operate on meaning representations of documents have been neglected in the past, although they are a very promising and interesting class of methods for summarization and text understanding. In this thesis, I present one such summarizer, which uses the proposition as its meaning representation. My summarizer is an implementation of Kintsch and van Dijk's model of comprehension, which uses a tree of propositions to represent the working memory. The input document is processed incrementally in iterations. In each iteration, new propositions are connected to the tree under the principle of local coherence, and then a forgetting mechanism is applied so that only a few important propositions are retained in the tree for the next iteration. A summary can be generated using the propositions which are frequently retained. Originally, this model was only played through by hand by its inventors using human-created propositions. In this work, I turned it into a fully automatic model using current NLP technologies. First, I create propositions by obtaining and then transforming a syntactic parse. Second, I have devised algorithms to numerically evaluate alternative ways of adding a new proposition, as well as to predict necessary changes in the tree. Third, I compared different methods of modelling local coherence, including coreference resolution, distributional similarity, and lexical chains. In the first group of experiments, my summarizer realizes summary propositions by sentence extraction. These experiments show that my summarizer outperforms several state-of-the-art summarizers. The second group of experiments concerns abstractive generation from propositions, which is a collaborative project. I have investigated the option of compressing extracted sentences, but generation from propositions has been shown to provide better information packaging

Linking named entities to Wikipedia

Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems

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

Strategies to Address Data Sparseness in Implicit Semantic Role Labeling

Natural language texts frequently contain predicates whose complete understanding re- quires access to other parts of the discourse. Human readers can retrieve such infor- mation across sentence boundaries and infer the implicit piece of information. This capability enables us to understand complicated texts without needing to repeat the same information in every single sentence. However, for computational systems, resolv- ing such information is problematic because computational approaches traditionally rely on sentence-level processing and rarely take into account the extra-sentential context. In this dissertation, we investigate this omission phenomena, called implicit semantic role labeling. Implicit semantic role labeling involves identification of predicate argu- ments that are not locally realized but are resolvable from the context. For example, in ”What’s the matter, Walters? asked Baynes sharply.”, the ADDRESSEE of the predicate ask, Walters, is not mentioned as one of its syntactic arguments, but can be recoverable from the previous sentence. In this thesis, we try to improve methods for the automatic processing of such predicate instances to improve natural language pro- cessing applications. Our main contribution is introducing approaches to solve the data sparseness problem of the task. We improve automatic identification of implicit roles by increasing the amount of training set without needing to annotate new instances. For this purpose, we propose two approaches. As the first one, we use crowdsourcing to annotate instances of implicit semantic roles and show that with an appropriate task de- sign, reliable annotation of implicit semantic roles can be obtained from the non-experts without the need to present precise and linguistic definition of the roles to them. As the second approach, we combine seemingly incompatible corpora to solve the problem of data sparseness of ISRL by applying a domain adaptation technique. We show that out of domain data from a different genre can be successfully used to improve a baseline implicit semantic role labeling model, when used with an appropriate domain adapta- tion technique. The results also show that the improvement occurs regardless of the predicate part of speech, that is, identification of implicit roles relies more on semantic features than syntactic ones. Therefore, annotating instances of nominal predicates, for instance, can help to improve identification of verbal predicates’ implicit roles, we well. Our findings also show that the variety of the additional data is more important than its size. That is, increasing a large amount of data does not necessarily lead to a better model