Coreference Resolution: To What Extent Does It Help NLP Applications?

Mitkov, Ruslan; Evans, Richard J.; Orăsan, Constantin; Dornescu, Iustin; Rios, Miguel

doi:10.1007/978-3-642-32790-2_2

Cited by 9 publications

(11 citation statements)

References 24 publications

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“…The final results were analysed regarding: Table 1 reports the mean scores of the user evaluations, the variance, and p-value for each characteristic. The results show that the difference between the two approaches (TR and TR + AR) is not statistically significant, which coincides with results obtained by Mitkov et al [6]. However, from the results obtained in our research, we can notice a pattern.…”

Section: Resultssupporting

confidence: 35%

“…Anaphora is a reference which points back to some previous item, with the 'pointing back' word or phrase called an anaphor, and the entity to which it refers, or for which it stands, its antecedent [6]. For example: "The boy is clever, he is the first in the class".…”

Section: Related Workmentioning

confidence: 99%

“…As a result, diverse NLP techniques have been combined together in an attempt to deliver advancements in the process, with mixed results. While some combinations of techniques demonstrated positive impact [5], some did not [6].…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have combined AR with the summarisation algorithm, and their results have shown a positive impact on summaries [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Towards Evaluating the Impact of Anaphora Resolution on Text Summarisation from a Human Perspective

Bayomi

Levacher

Ghorab

et al. 2016

Natural Language Processing and Information Systems

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Abstract. Automatic Text Summarisation (TS) is the process of abstracting key content from information sources. Previous research attempted to combine diverse NLP techniques to improve the quality of the produced summaries. The study reported in this paper seeks to establish whether Anaphora Resolution (AR) can improve the quality of generated summaries, and to assess whether AR has the same impact on text from different subject domains. Summarisation evaluation is critical to the development of automatic summarisation systems. Previous studies have evaluated their summaries using automatic techniques. However, automatic techniques lack the ability to evaluate certain factors which are better quantified by human beings. In this paper the summaries are evaluated via human judgment, where the following factors are taken into consideration: informativeness, readability and understandability, conciseness, and the overall quality of the summary. Overall, the results of this study depict a pattern of slight but not significant increases in the quality of summaries produced using AR. At a subject domain level, however, the results demonstrate that the contribution of AR towards TS is domain dependent and for some domains it has a statistically significant impact on TS.

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Section: Resultssupporting

confidence: 35%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A number of studies have combined AR with the summarisation algorithm, and their results have shown a positive impact on summaries [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Towards Evaluating the Impact of Anaphora Resolution on Text Summarisation from a Human Perspective

Bayomi

Levacher

Ghorab

et al. 2016

Natural Language Processing and Information Systems

View full text Add to dashboard Cite

show abstract

“…The CBOW architecture tends to have better performance in discovering syntactic regularities as compared to semantic regularities, whereas the skip-gram architecture tends to have a higher performance in finding semantic regularities rather than syntactic ones (Mitkov et al, 2012;Pennington et al, 2014). Because we are dealing with unigram terms and not multi-word terms at this stage, we expect a skip-gram filter to outperform a CBOW filter.…”

Section: Specificity Vectorsmentioning

confidence: 99%

Representation Learning for Information Extraction

Amjadian¹

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Distributed representations, predominantly acquired via neural networks, have been applied to natural language processing tasks including speech recognition and machine translation with a success comparable to sophisticated state-of-the-art algorithms. The present thesis offers an investigation of the application of such representations to information extraction. Specifically, I explore the suitability of applying shallow distributed representations to the automatic terminology extraction task, as well as the bridging reference resolution task. I created a dataset as a gold standard for automatic term extraction in the mathematical education domain. I carefully assessed the performance of the existing terminology extraction methods on this dataset. Then, I introduce a novel method for automatic terminology extraction for one word terms, and I evaluate the performance of the novel algorithm in various terminological domains. The introduced algorithm leverages the distributed representation of words from the local and global perspectives to encode syntactic, semantic, association, and frequency information at the same time. Furthermore, this novel algorithm can be trained with a minimal number of data points. I show that the algorithm is robust to the change of domain, and that information can be transferred from one technical domain to another, leveraging what we call anchor words with consistent semantics shared between the domains. As for the bridging reference resolution task, a dataset is built on the letter portion of the Open American National Corpus and I compare the performance of a preliminary method against a majority class baseline.

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