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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 2020

While many languages use adpositions to encode semantic relationships between content words in a sentence (e.g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning. In this paper, we empirically adapt the SNACS framework (Schneider et al., 2018) to Korean, a language that is typologically distant from English—the language SNACS was originally designed upon. We apply the SNACS framework to annotate the highly popular novella The Little Prince with semantic supersense labels over all Korean postpositions. Thus, we introduce the first broad-coverage corpus annotated with Korean postposition semantics and provide a detailed analysis of the corpus with an apples-to-apples comparison between Korean and English annotations.

K-SNACS: Annotating Korean Adposition Semantics

technical paper

Representing constructional metaphors

A Continuation Semantics for Abstract Meaning
Representation

InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research

Cross-lingual annotation: a road map for low- and no-resource languages

Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension

How do image description systems describe people? A targeted assessment of system competence in the PEOPLE-domain

Multilingual Emoticon Prediction of Tweets about COVID-19

Persuasiveness of News Editorials depending on Ideology and Personality

Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality

https://d3smihljt9218e.cloudfront.net/transcript/4494/subtitle/98de61acb5cdede7413ef1bde54b5cd8.vtt

English (automatic)

K-SNACS: Annotating Korean Adposition Semantics

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Representing constructional metaphors

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