Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text. While making such an assessment is trivial for humans, constructing algorithms and computational models that mimic human level performance represents a difficult and deep natural language understanding problem. The 2017 STS shared task involves multilingual and cross-lingual evaluation of Arabic, Spanish and English data as well as a surprise language track to explore methods for cross-lingual transfer.
SemEval 2014 - Task 3 Cross-Level Semantic Similarity
The aim of this task is to evaluate semantic similarity when comparing lexical items of different types, such as paragraphs, sentences, phrases, words, and senses.
Seventeenth Conference on Computational Natural Language Learning
August 8-9, 2013
Final Call for Papers
CoNLL is the yearly conference organized by SIGNLL (the ACL Special
Interest Group on Natural Language Learning). This year, CoNLL will be