Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Word Embeddings: Stability and Semantic Change   Word embeddings are computed by a class of techniques within natural language
processing (NLP), that create continuous vector representations of words in a
language from a large text corpus. The stochastic nature of the training
process of most embedding techniques can lead to surprisingly strong
instability, i.e. subsequently applying the same technique to the same data
twice, can produce entirely different results. In this work, we present an
experimental study on the instability of the training process of three of the
most influential embedding techniques of the last decade: word2vec, GloVe and
fastText. Based on the experimental results, we propose a statistical model to
describe the instability of embedding techniques and introduce a novel metric
to measure the instability of the representation of an individual word.
Finally, we propose a method to minimize the instability - by computing a
modified average over multiple runs - and apply it to a specific linguistic
problem: The detection and quantification of semantic change, i.e. measuring
changes in the meaning and usage of words over time.

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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat