Finding Individual Word Sense Changes and their Delay in Appearance

We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change.

[1]  Steven Skiena,et al.  Statistically Significant Detection of Linguistic Change , 2014, WWW.

[2]  Marco Baroni,et al.  A distributional similarity approach to the detection of semantic change in the Google Books Ngram corpus. , 2011, GEMS.

[3]  Daniel Jurafsky,et al.  Do Multi-Sense Embeddings Improve Natural Language Understanding? , 2015, EMNLP.

[4]  Eyal Sagi,et al.  Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space , 2009 .

[5]  Slav Petrov,et al.  Temporal Analysis of Language through Neural Language Models , 2014, LTCSS@ACL.

[6]  Thomas Risse,et al.  On the applicability of word sense discrimination on 201 years of modern english , 2013, International Journal on Digital Libraries.

[7]  Christian Biemann,et al.  That’s sick dude!: Automatic identification of word sense change across different timescales , 2014, ACL.

[8]  Annalina Caputo,et al.  Diachronic Analysis of the Italian Language exploiting Google Ngram , 2016, CLiC-it/EVALITA.

[9]  Christian Biemann,et al.  Making Sense of Word Embeddings , 2016, Rep4NLP@ACL.

[10]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[11]  Jure Leskovec,et al.  Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change , 2016, ACL.

[12]  John Price-Wilkin,et al.  Oxford English Dictionary (2nd ed.) , 1991 .

[13]  D. Wijaya,et al.  Understanding semantic change of words over centuries , 2011, DETECT '11.

[14]  ChengXiang Zhai,et al.  Discovering evolutionary theme patterns from text: an exploration of temporal text mining , 2005, KDD '05.

[15]  Adam Jatowt,et al.  Detecting Evolution of Concepts based on Cause-Effect Relationships in Online Reviews , 2016, WWW.

[16]  Mirella Lapata,et al.  A Bayesian Model of Diachronic Meaning Change , 2016, TACL.

[17]  Christian Biemann,et al.  An automatic approach to identify word sense changes in text media across timescales , 2015, Natural Language Engineering.

[18]  Patrick Pantel,et al.  Discovering word senses from text , 2002, KDD.

[19]  Nina Tahmasebi Models and Algorithms for Automatic Detection of Language Evolution , 2013 .

[20]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[21]  Timothy Baldwin,et al.  Word Sense Induction for Novel Sense Detection , 2012, EACL.

[22]  Martin C. Cooper A Mathematical Model of Historical Semantics and the Grouping of Word Meanings into Concepts , 2005, Computational Linguistics.

[23]  Dominic Widdows,et al.  Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination , 2004 .

[24]  John Liu,et al.  sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings , 2015, ArXiv.

[25]  Dekang Lin,et al.  Automatic Retrieval and Clustering of Similar Words , 1998, ACL.