Contextual word representations

Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.

7p visteverogers 24-06-2023 5 1   Download

The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology.

17p vicolorado2711 23-10-2020 23 1   Download

Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers.

15p vicolorado2711 22-10-2020 19 0   Download

This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of child-directed utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model.

11p bunthai_1 06-05-2013 52 2   Download

Collocational word similarity is considered a source of text cohesion that is hard to measure and quantify. The work presented here explores the use of information from a training corpus in measuring word similarity and evaluates the method in the text segmentation task. An implementation, the V e c T i l e system, produces similarity curves over texts using pre-compiled vector representations of the contextual behavior of words. The performance of this system is shown to improve over that of the purely string-based TextTiling algorithm (Hearst, 1997). 1 Background ...

5p bunrieu_1 18-04-2013 56 3   Download

Turkish is an agglutinative language with complex morphological structures, therefore using only word forms is not enough for many computational tasks. In this paper we analyze the effect of morphology in a Named Entity Recognition system for Turkish. We start with the standard word-level representation and incrementally explore the effect of capturing syntactic and contextual properties of tokens. Furthermore, we also explore a new representation in which roots and morphological features are represented as separate tokens instead of representing only words as tokens. ...

6p hongdo_1 12-04-2013 49 3   Download