1,379 research outputs found

    Structured Representations for Coreference Resolution

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    Coreference resolution is the task of determining which expressions in a text are used to refer to the same entity. This task is one of the most fundamental problems of natural language understanding. Inherently, coreference resolution is a structured task, as the output consists of sets of coreferring expressions. This complex structure poses several challenges since it is not clear how to account for the structure in terms of error analysis and representation. In this thesis, we present a treatment of computational coreference resolution that accounts for the structure. Our treatment encompasses error analysis and the representation of approaches to coreference resolution. In particular, we propose two frameworks in this thesis. The first framework deals with error analysis. We gather requirements for an appropriate error analysis method and devise a framework that considers a structured graph-based representation of the reference annotation and the system output. Error extraction is performed by constructing linguistically motivated or data-driven spanning trees for the graph-based coreference representations. The second framework concerns the representation of approaches to coreference resolution. We show that approaches to coreference resolution can be understood as predictors of latent structures that are not annotated in the data. From these latent structures, the final output is derived during a post-processing step. We devise a machine learning framework for coreference resolution based on this insight. In this framework, we have a unified representation of approaches to coreference resolution. Individual approaches can be expressed as instantiations of a generic approach. We express many approaches from the literature as well as novel variants in our framework, ranging from simple pairwise classification approaches to complex entity-centric models. Using the uniform representation, we are able to analyze differences and similarities between the models transparently and in detail. Finally, we employ the error analysis framework to perform a qualitative analysis of differences in error profiles of the models on a benchmark dataset. We trace back differences in the error profiles to differences in the representation. Our analysis shows that a mention ranking model and a tree-based mention-entity model with left-to-right inference have the highest performance. We discuss reasons for the improved performance and analyze why more advanced approaches modeled in our framework cannot improve on these models. An implementation of the frameworks discussed in this thesis is publicly available

    A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

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    A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

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    We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020Comment: Accepted for CRAC 2020 @ COLIN

    Query-Driven Sampling for Collective Entity Resolution

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    Probabilistic databases play a preeminent role in the processing and management of uncertain data. Recently, many database research efforts have integrated probabilistic models into databases to support tasks such as information extraction and labeling. Many of these efforts are based on batch oriented inference which inhibits a realtime workflow. One important task is entity resolution (ER). ER is the process of determining records (mentions) in a database that correspond to the same real-world entity. Traditional pairwise ER methods can lead to inconsistencies and low accuracy due to localized decisions. Leading ER systems solve this problem by collectively resolving all records using a probabilistic graphical model and Markov chain Monte Carlo (MCMC) inference. However, for large datasets this is an extremely expensive process. One key observation is that, such exhaustive ER process incurs a huge up-front cost, which is wasteful in practice because most users are interested in only a small subset of entities. In this paper, we advocate pay-as-you-go entity resolution by developing a number of query-driven collective ER techniques. We introduce two classes of SQL queries that involve ER operators --- selection-driven ER and join-driven ER. We implement novel variations of the MCMC Metropolis Hastings algorithm to generate biased samples and selectivity-based scheduling algorithms to support the two classes of ER queries. Finally, we show that query-driven ER algorithms can converge and return results within minutes over a database populated with the extraction from a newswire dataset containing 71 million mentions

    A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

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    We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020Comment: Accepted for CRAC 2020 @ COLIN

    Improved Coreference Resolution Using Cognitive Insights

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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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