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arxiv: 1405.3515 · v1 · pith:BMMJZL7Fnew · submitted 2014-05-14 · 💻 cs.CL

Temporal Analysis of Language through Neural Language Models

classification 💻 cs.CL
keywords languagemodelwordschangechangedduringidentifiesneural
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We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to obtain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. The model identifies words such as "cell" and "gay" as having changed during that time period. The model simultaneously identifies the specific years during which such words underwent change.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Diachronic Embedding for Temporal Knowledge Graph Completion

    cs.LG 2019-07 unverdicted novelty 6.0

    Proposes a model-agnostic diachronic entity embedding function to extend static KG embedding models for temporal knowledge graph completion, with a proof that the SimplE combination is fully expressive.