Semantic frames and semantic networks in the Health Science Corpus

[eng] The aim of this paper is to apply frame semantics principles to the analysis of a specialized corpus, the Health Science Corpus, implemented in the lexical database SciE-Lex. Taking FrameNet as the basis for this research, I will assign frame semantic features to Scie-Lex data in order to highlight the shared semantic and syntactic background of the related words in the biomedical register, give motivation to their patterns of collocates and establish frame-based semantic networks of related lexical units.[spa] El objetivo de este artículo es aplicar los principios de la semántica de marcos al análisis de un corpus especializado, el Health Science Corpus, implementado en la base de datos léxica SciE-Lex. Tomando FrameNet como base para esta investigación, se aplica la semántica de marcos a los datos de Scie-Lex para destacar los aspectos sintácticos y semánticos communes de los términos del registro biomédico, motivar sus patrones combinatorios y establecer redes semánticas basadas en marcos

Semantic frames and semantic networks in the Health Science Corpus

The aim of this paper is to apply frame semantics principles to the analysis of a specialized corpus, the Health Science Corpus, implemented in the lexical data b ase SciE-Lex. Taking FrameNet as the basis for this research, I will assign frame semantic features to Scie-Lex data in order to highlight the shared semantic and syntactic background of the related words in the biomedical register, give motivation to their patterns of collocates and establish frame-based semantic networks of related lexical units.El objetivo de este artículo es aplicar los principios de la semántica de marcos al análisis de un corpus especializado, el Health Science Corpus, implementado en la base de datos léxica SciE-Lex. Tomando FrameNet como base para esta investigación, se aplica la semántica de marcos a los datos de Scie-Lex para destacar los aspectos sintácticos y semánticos communes de los términos del registro biomédico, motivar sus patrones combinatorios y establecer redes semánticas basadas en marcos

HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.Comment: 5 pages, 3 tables, accepted at SemEval 201

Non-distributional Word Vector Representations

Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We analyze their performance on state-of-the-art evaluation methods for distributional models of word vectors and find they are competitive to standard distributional approaches.Comment: Proceedings of ACL 201