Spain

The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa&#39;s notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa&#39;s approach with the simplicity of Allen&#39;s notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary &quot;relType&quot; type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.

COLING 2020

Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Bounding Relations

bounding relations

timeml

temporal relations

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa's notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa's approach with the simplicity of Allen's notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary "relType" type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.

poster

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Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.

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Narrow specialized comparable corpora are often small in size. This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of-domain resources. Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.

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This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration.

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Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Bounding Relations

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Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Bounding Relations

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Next from COLING 2020

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