Semanlink - sl:creationDate : 2018-05-10 AND TheWebConf 2018

Improving Word Embedding Compositionality using Lexicographic Definitions

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2018-05-10 About

That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information

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post-processing method for generating low-dimensional sense embedding. Emploies the ontological and contextual information simultaneously.

(Poster at the Web Conf) [Github](https://github.com/y95847frank/Joint-Retrofitting)

Calcule des "sense embeddings", en partant de word embeddings pré-calculés (par ex avec word2vec), et de données de type lexicographiques (ex wordnet), en contraignant, pour un sens, la distance entre sense et word embedding.

Abstract:

> While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits the word representation into sense representation from contextual and ontological information. The experiments show the effectiveness and robustness of our model that outperforms previous approaches in four public available benchmark datasets.

> Given a trained word embedding and a lexical ontology that contains sense level relationships (e.g., synonym, hypernym, etc.), our model generates new sense vectors via constraining the distance between the sense vector and its word form vector, its sense neighbors and its contextual neighbors

[Influenced by](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1411.4166) (which post-processes and modifies word vectors to incorporate knowledge from semantic lexicons, while this creates new sense vectors)

2018-05-10 About

Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

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2018-05-10 About

TUTORIAL: Graph-based Text Representations (SLIDES)

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2018-05-10 About

TUTORIAL: Graph-based Text Representations: Boosting Text Mining, NLP and Information Retrieval with Graphs

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2018-05-10 About