Can word vectors help corpus linguists?

Corpus-independent Generic Keyphrase Extraction Using Word Embedding Vectors

Keyphrase extraction from a given document is a difficult task that requires not only local statistical information but also extensive background knowledge. In this paper, we propose a graph-based ranking approach that uses information supplied by word embedding vectors as the background knowledge. We first introduce a weighting scheme that computes informativeness and phraseness scores of word...

Draft WebCorp: providing a renewable data source for corpus linguists

The many electronic text corpora available nowadays present ever fewer obstacles to a wide range of corpus linguistic study. However, corpora are expensive resources to create and to update, and there remain problems for linguists if they seek access to very large, very recent, or changing language. The World Wide Web, whilst intended as an information source, is an obvious resource for the ret...

Can Syntactic and Logical Graphs help Word Sense Disambiguation?

This paper presents a word sense disambiguation (WSD) approach based on syntactic and logical representations. The objective here is to run a number of experiments to compare standard contexts (word windows, sentence windows) with contexts provided by a dependency parser (syntactic context) and a logical analyzer (logico-semantic context). The approach presented here relies on a dependency gram...

Developing AntConc for a new generation of corpus linguists

How far can prosodic cues help in word segmentation?

Prosodic cues are of great importance in parsing speech signal into prosodic and lexical units. Listeners detect the changes of the prosodic parameters and interpret them to detect sentence modalities or the mood of the speaker. Some automatic speech recognition systems try to use prosodic parameters to detect boundaries of prosodic units and help thus the acoustic decoding process. Although th...