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

By Sunny Mitra, Ritwik Mitra, Martin Riedl, Chris Biemann, Animesh Mukherjee, Pawan Goyal. 2014

104 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cultural Studies, Natural Language Processing, Computational Linguistics & Semantics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cultural Studies

In Natural Language Processing

In Computational Linguistics & Semantics

Citations in the corpus

Analysing Lexical Semantic Change with Contextualised Word Representations (2020, background): “This limitation has motivated recent work on word sense induction across time periods (Lau et al., 2012; Cook et al., 2014; Mitra et al., 2014; Frermann and Lap-ata, 2016; Rudolph and Blei, 2018; Hu et al., 2019).”
An automatic approach to identify word sense changes in text media across timescales (2015, background): “The work presented here is an extension of Mitra et al. (2014).”
Neural context embeddings for automatic discovery of word senses (2015, background): “WSI is of particular interest in situations where standard lexical resources are unreliable or inapplicable, such as when tracking changes of word meaning over time (Mitra et al., 2014).”
A Bayesian Model of Diachronic Meaning Change (2016, methodology): “Our accuracy scores are in the same ballpark as Mitra et al. (2014, 2015).”
Diachronic word embeddings and semantic shifts: a survey (2018, background): “This problem was to some degree addressed by Mitra et al. (2014), but much more work is certainly required to empirically test classiﬁcation schemes proposed in much of the theoretical work described in Section 2.”
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (2019, unknown): “…embed-dings (Kim et al., 2014; Basile et al., 2016; Kulka-rni et al., 2015; Hamilton et al., 2016b); graph-based models (Tahmasebi and Risse, 2017a; Mitra et al., 2014, 2015); and ﬁnally topic-based (Lau et al., 2012; Wang et al., 2015; Frermann and La-pata, 2016; Hengchen, 2017; Perrone et al.,…”
Statistically Significant Detection of Linguistic Change (2014, methodology): “[29] use a graph based approach relying on dependency parsing of sentences.”