Task Driven Coreference Resolution for Relation Extraction

2004

Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02, 2001

We present a noun phrase coreference system that extends the work of Soon et al. (2001) and, to our knowledge, produces the best results to date on the MUC-6 and MUC-7 coreference resolution data sets-F-measures of 70.4 and 63.4, respectively. Improvements arise from two sources: extra-linguistic changes to the learning framework and a large-scale expansion of the feature set to include more sophisticated linguistic knowledge.

Proceedings of the 2013 workshop on Automated knowledge base construction, 2013

Journal of Information Systems and Telecommunication (JIST), 2017

Coreference resolution is the problem of clustering mentions in a text that refer to the same entities, and is a crucial and difficult step in every natural language processing task. Despite the efforts that have been made to solve this problem during the past, its performance still does not meet today's application requirements. Given the importance of the verbs in sentences, in this work, we tried to incorporate three types of their information on coreference resolution problem, namely, selectional restriction of verbs on their arguments, semantic relation between verb pairs, and the truth that arguments of a verb cannot be coreferent of each other. As a needed resource for supporting our model, we generate a repository of semantic relations between verb pairs automatically using Distributional Memory (DM), a state-of-the-art framework for distributional semantics. This resource consists of pairs of verbs associated with their probable arguments, their role mapping, and significance scores based on our measures. Our proposed model for coreference resolution encodes verb's knowledge with Markov logic network rules on top of the deterministic Stanford coreference resolution system. Experiment results show that this semantic layer can improve the recall of the Stanford system while preserves its precision and improves it slightly.

CoNLL 2011, 2011

Studies in Corpus Linguistics, 2000

Algorithms for performing coreference resolution can only be precisely evaluated given a benchmark corpus of coreference-annotated texts, together with techniques for evaluating the algorithms' output against the corpus. Such a corpus and such techniques have become available for the rst time as part of the Message Understanding Conference 6 (MUC-6) evaluations of information extraction systems. In this paper we describe the MUC-6 coreference task and the approach to taken to it by the Large Scale Information Extraction (LaSIE) system developed at the University of She eld. The basic coreference algorithm used by this system is described in detail, as well as a set of variants, which allow us to experiment with di erent constraints such as restrictions to certain classes of anaphor, distance restrictions between anaphor and antecedent, and weighting factors in assessing semantic similarity of potential coreferents. Quantitative evaluation results are presented for these variants, demonstrating both the utility of quantative analysis for assessing coreference algorithms and the exibility of our approach to coreference which provides a framework that facilitates experimentation with alternative techniques.

Language Resources and Evaluation, 2008

We present a knowledge-based coreference resolution system for noun phrases in Hungarian texts. The system is used as a module in an automated psychological text processing project. Our system uses rules that rely on knowledge from the morphological, syntactic and semantic output of a deep parser and semantic relations form the Hungarian WordNet ontology. We also use rules that rely on Binding Theory, research results in Hungarian psycholinguistics, current research on proper name coreference identification and our own heuristics. We describe the constraints-and-preferences algorithm in detail that attempts to find coreference information for proper names, common nouns, pronouns and zero pronouns in texts. We present evaluation results for our system on a corpus manually annotated with coreference relations. Precision of the resolution of various coreference types reaches up to 80%, while overall recall is 63%. We also present an investigation of the various error types our system produced, along with an analysis of the results.

2004

In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we make two extensions to the standard learn-ing-based resolution framework. First, to improve the recall rate, we introduce an additional set of features to capture the different matching patterns between noun phrases. Second, to improve the precision, we modify the instance selection strategy to allow non-anaphors to be included during training instance generation. The evaluation done on MEDLINE data set shows that the combination of the two extensions provides significant gains in the F-measure.

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance. Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list. Results demonstrate an increase of up to 5% F1-score for the evaluated IE tasks on two datasets. Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach.