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On the linearity of semantic change: Investigating Meaning Variation via Dynamic Graph Models

Eger, Steffen ; Mehler, Alexander (2016)
On the linearity of semantic change: Investigating Meaning Variation via Dynamic Graph Models.
54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany (07.08.2016-12.08.2016)
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word's meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today's embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2016
Autor(en): Eger, Steffen ; Mehler, Alexander
Art des Eintrags: Bibliographie
Titel: On the linearity of semantic change: Investigating Meaning Variation via Dynamic Graph Models
Sprache: Englisch
Publikationsjahr: August 2016
Ort: Berlin
Verlag: Association for Computational Linguistics
Buchtitel: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)
Veranstaltungstitel: 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)
Veranstaltungsort: Berlin, Germany
Veranstaltungsdatum: 07.08.2016-12.08.2016
URL / URN: http://www.aclweb.org/anthology/P16-2009
Kurzbeschreibung (Abstract):

We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word's meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today's embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change.

Freie Schlagworte: UKP_a_DLinNLP;Semantic Change, Word Embeddings
ID-Nummer: TUD-CS-2016-0172
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
Hinterlegungsdatum: 31 Dez 2016 14:29
Letzte Änderung: 02 Jul 2024 09:38
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