Proceedings of the LexSem+Logics Workshop 2016

TL;DR

The paper summarizes the 2016 LexSem+Logics workshop, highlighting advances in lexical semantics, resource creation, and formal tools, with applications across multiple languages and NLP tasks.

Contribution

It reports on combined workshop topics, including resource encoding, semantic similarity, cross-lingual annotations, and new strategies for open information extraction.

Findings

Encoding of plurals in Wordnets

Creation of a semantic similarity-based thesaurus

Use of cross-lingual treebanks for POS tagging

Abstract

Lexical semantics continues to play an important role in driving research directions in NLP, with the recognition and understanding of context becoming increasingly important in delivering successful outcomes in NLP tasks. Besides traditional processing areas such as word sense and named entity disambiguation, the creation and maintenance of dictionaries, annotated corpora and resources have become cornerstones of lexical semantics research and produced a wealth of contextual information that NLP processes can exploit. New efforts both to link and construct from scratch such information - as Linked Open Data or by way of formal tools coming from logic, ontologies and automated reasoning - have increased the interoperability and accessibility of resources for lexical and computational semantics, even in those languages for which they have previously been limited. LexSem+Logics 2016 combines the 1st Workshop on Lexical Semantics for Lesser-Resources Languages and the 3rd Workshop on Logics and Ontologies. The accepted papers in our program covered topics across these two areas, including: the encoding of plurals in Wordnets, the creation of a thesaurus from multiple sources based on semantic similarity metrics, and the use of cross-lingual treebanks and annotations for universal part-of-speech tagging. We also welcomed talks from two distinguished speakers: on Portuguese lexical knowledge bases (different approaches, results and their application in NLP tasks) and on new strategies for open information extraction (the capture of verb-based propositions from massive text corpora).