Konferenzbeitrag
Transparent, efficient, and robust word embedding access with WOMBAT
We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.
- Sprache
-
Englisch
- Thema
-
Python <Programmiersprache>
Automatische Sprachanalyse
Code
Computerlinguistik
Sprache
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Müller, Mark-Christoph
Strube, Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
Stroudsburg, Pennsylvania : Association for Computational Linguistics
Mannheim : Leibniz-Institut für Deutsche Sprache (IDS)
- (wann)
-
2022-06-14
- URN
-
urn:nbn:de:bsz:mh39-110862
- Letzte Aktualisierung
-
06.03.2025, 09:00 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Konferenzbeitrag
Beteiligte
- Müller, Mark-Christoph
- Strube, Michael
- Stroudsburg, Pennsylvania : Association for Computational Linguistics
- Mannheim : Leibniz-Institut für Deutsche Sprache (IDS)
Entstanden
- 2022-06-14
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