That's sick dude!: Automatic identification of word sense change across different timescales

Abstract

In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. 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 compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 randomly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.

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

“…Some of our important findings from this study are: (i) the number of candidate senses produced by McCarthy et al (2004) is far less than the two other methods, (ii) Mitra et al (2014) produces the best representative sense cluster for a word in the time period 2006-2008and McCarthy et al (2004 produces the best representative sense cluster for a word in the time period 1987-1995, (iii) Mitra et al (2014) is able to identify sense differences more accurately in comparison to the other methods, (iv) considering both the aspects together, McCarthy et al (2004) performs the best, (v) for the common results produced by Lau et al (2014) and Mitra et al (2014), the former does better sense differentiation while the latter does better overall.…”

“…In principle, we compare all the senses of a word in one corpus against all the senses of the same word in another corpus. We, therefore, base this work on three different approaches, Mitra et al (2014), Lau et al (2014) and McCarthy et al (2004), which could be adapted to find word senses in different corpora in an unsupervised manner. Next, we discuss these methods briefly followed by the pro-posed adaptation technique and generation of the candidate set.…”

“…Thus, for each cluster s bi , we identify the fraction of words that are not present in any of the |N | newspaper clusters. If this value is above a threshold, we call s bi a book-specific sense cluster for the word w. This threshold has been set to 0.8 for all the experiments, as also reported in Mitra et al (2014).…”

“…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. Manual evaluation conducted using 60 candidate words for each method indicates that ∼45-60% of the corpus-specific senses identified by the adapted algorithms are genuine.…”

Detecting and Assessing Contextual Change in Diachronic Text Documents using Context Volatility

“…The approach models semantic change over time by setting a certain time period as reference point and comparing a latent semantic space to that reference over time. (Fernndez-Silva et al, 2011;Mitra et al, 2014) or (Picton, 2011) look for linguistic clues and different patterns of variation to better understand the dynamics of terms. The popular word2vec model has also been utilized for tracking changes in vocabulary contexts (Kim et al, 2014;Kenter et al, 2015).…”