Deep learning embeddings for discontinuous linguistic units

Wenpeng Yin; Hinrich Schütze

Deep learning embeddings for discontinuous linguistic units

Wenpeng Yin, Hinrich Schütze

Computer Science and Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although a number of recent papers have extended this to other linguistic units like morphemes and phrases. In this paper, we argue that learning embeddings for discontinuous linguistic units should also be considered. In an experimental evaluation on coreference resolution, we show that such embeddings perform better than word form embeddings.

Original language	English (US)
State	Published - 2014
Event	2nd International Conference on Learning Representations, ICLR 2014 - Banff, Canada Duration: Apr 14 2014 → Apr 16 2014

Conference

Conference	2nd International Conference on Learning Representations, ICLR 2014
Country/Territory	Canada
City	Banff
Period	4/14/14 → 4/16/14

All Science Journal Classification (ASJC) codes

Computer Science Applications
Linguistics and Language
Language and Linguistics
Education

Cite this

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title = "Deep learning embeddings for discontinuous linguistic units",

abstract = "Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although a number of recent papers have extended this to other linguistic units like morphemes and phrases. In this paper, we argue that learning embeddings for discontinuous linguistic units should also be considered. In an experimental evaluation on coreference resolution, we show that such embeddings perform better than word form embeddings.",

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AU - Yin, Wenpeng

AU - Schütze, Hinrich

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Y1 - 2014

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AB - Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although a number of recent papers have extended this to other linguistic units like morphemes and phrases. In this paper, we argue that learning embeddings for discontinuous linguistic units should also be considered. In an experimental evaluation on coreference resolution, we show that such embeddings perform better than word form embeddings.

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M3 - Paper

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T2 - 2nd International Conference on Learning Representations, ICLR 2014

Y2 - 14 April 2014 through 16 April 2014

ER -

Deep learning embeddings for discontinuous linguistic units

Abstract

Conference

All Science Journal Classification (ASJC) codes

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Cite this