An automatic approach to identify word sense changes in text media across timescales

Abstract

In this paper, we propose an unsupervised and automated method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books and millions of tweets posted per day. We construct distributional-thesauri-based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we propose a split/join based approach to compare the sense clusters at two different time points to find if there is ‘birth’ of a new sense. The approach also helps us to find if an older sense was ‘split’ into more than one sense or a newer sense has been formed from the ‘join’ of older senses or a particular sense has undergone ‘death’. We use this completely unsupervised approach (a) within the Google books data to identify word sense differences within a media, and (b) across Google books and Twitter data to identify differences in word sense distribution across different media. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet.

The natural selection of words: Finding the features of fitness

“…As we mentioned in the introduction, several papers consider how words change their meanings over time [10][11][12][13]. For example, Mihalcea and Nastase [10] discuss the shift in meaning of gay, from expressing an emotion to specifying a sexual orientation.…”

“…However, we prefer to take the more conservative approach of only allowing monosemous words, since it does not require us to us to assume that we can ignore the impact of secondary senses on the evolution of a synset. The ideal solution to bridging the gap between GBNC and WordNet would be to automatically sense-tag all of the words in GBNC, but this would involve a major effort, requiring the cooperation of Google. In the section on related work, we mentioned past research concerned with how words change their meanings over time (same word, new meaning) [10][11][12][13]. Let's call this meaning-change.…”

“…A number of recent papers have examined the problem of understanding how words change their meanings over time [10][11][12][13]. In contrast, we examine the problem of understanding how meanings (synsets) change their words over time.…”

A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains

“…LSC of a word is measured by calculating a novelty score for its senses based on their frequency of use. (iii) Clustering models assign all uses of a word into sense clusters based on some contextual property (Mitra et al, 2015). Word sense clustering models are similar to topic models in that they map uses to senses.…”

Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences

“…It is an important as well as challenging task to identify predominant word senses specific to various corpora. While the researchers have started exploring the temporal and spatial scopes of word senses (Cook and Stevenson, 2010;Gulordava and Baroni, 2011;Kulkarni et al, 2015;Jatowt and Duh, 2014;Mitra et al, 2014;Mitra et al, 2015), corpora-specific senses have remained mostly unexplored. Our contributions: Motivated by the above applications, this paper studies corpora-specific senses for the first time and makes the following contributions 1 : (i) we take two different meth- 1 The code and evaluation results are available at: http: //tinyurl.com/h4onyww ods for novel sense discovery (Mitra et al, 2014;Lau et al, 2014) and one for predominant sense identification (McCarthy et al, 2004) and adapt these in an automated and unsupervised manner to identify corpus-specific sense for a given word (noun), and (ii) perform a thorough manual evaluation to rigorously compare the corpus-specific senses obtained using these methods.…”