Konferenzbeitrag
Semantic author name disambiguation with word embeddings
We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing for easy extensibility and domain adaptation. Initial experiments show that word embeddings can improve the Recall and F score of the binary classification sub-task of AND. Results for the clustering sub-task are less clear, but also promising and overall show the feasibility of the approach.
- Sprache
-
Englisch
- Thema
-
Maschinelles Lernen
Veröffentlichung
Deep learning
Semantik
Computerlinguistik
Sprache
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Müller, Mark-Christoph
- Ereignis
-
Veröffentlichung
- (wer)
-
Cham : Springer
Mannheim : Leibniz-Institut für Deutsche Sprache (IDS) [Zweitveröffentlichung]
- (wann)
-
2022-07-18
- URN
-
urn:nbn:de:bsz:mh39-111355
- 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
- Cham : Springer
- Mannheim : Leibniz-Institut für Deutsche Sprache (IDS) [Zweitveröffentlichung]
Entstanden
- 2022-07-18
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