Empirical distributional semantics: Methods and biomedical applications

Title: Empirical Distributional Semantics: Methods and Biomedical Applications 1.5 Rule-based Methods for Text Processing 2. Existing Applications of Distributional Semantics 2.4 Bilingual Information Extraction 2.7 Taxonomy Construction and Validation 3.1 Gene Clustering Using Medline Abstracts

Over the past fifteen years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retr...

Distributional Semantics Resources for Biomedical Text Processing

The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text ...

Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods

In this paper we present a word decompounding method that is based on distributional semantics. Our method does not require any linguistic knowledge and is initialized using a large monolingual corpus. The core idea of our approach is that parts of compounds (like “candle” and “stick”) are semantically similar to the entire compound, which helps to exclude spurious splits (like “candles” and “t...

Biomedical Signal Analysis: Contemporary Methods and Applications

Multimodal Distributional Semantics

Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, w...