Spain

spelling

sociolinguistics

word embeddings

social media

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 2020

Analyses of word embeddings have primarily focused on semantic and syntactic properties. However, word embeddings have the potential to encode other properties as well. In this paper, we propose a new perspective on the analysis of word embeddings by focusing on spelling variation. In social media, spelling variation is abundant and often socially meaningful. Here, we analyze word embeddings trained on Twitter and Reddit data. We present three analyses using pairs of word forms covering seven types of spelling variation in English. Taken together, our results show that word embeddings encode spelling variation patterns of various types to some extent, even embeddings trained using the skipgram model which does not take spelling into account. Our results also suggest a link between the intentionality of the variation and the distance of the non-conventional spellings to their conventional spellings.

Do Word Embeddings Capture Spelling Variation?

technical paper

SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers

Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

Probing Multimodal Embeddings for Linguistic Properties: the visual-semantic case

Parsers Know Best: German PP Attachment Revisited

Collective Wisdom: Improving Low-resource Neural Machine Translationusing Adaptive Knowledge Distillation

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

Complaint Identification in Social Media with Transformer Networks

Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering

Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction

A Graph Representation of Semi-structured Data for Web Question Answering - COLING2020

https://d3smihljt9218e.cloudfront.net/transcript/4137/subtitle/5dd185db1a16033a2f54a87d4ad2b73e.vtt

https://d3smihljt9218e.cloudfront.net/transcript/5051/subtitle/c28e1d53d79ed9a2ecf8e64a82c19512.vtt

English (automatic)

English

Do Word Embeddings Capture Spelling Variation?

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SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers

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