Konferenzbeitrag
EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification
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Dokumententyp
Text/Conference Paper
Dateien
Datum
2019
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Quelle
INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft
Data Science
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
In recent years, there has been an increasing interest in the task of relation classification, which aims to label a relation between two semantic entities. In this work, we investigate how domain-specific information influences the performance of ClaiRE, an SVM-based system combining manually crafted features with word embeddings. To this end, we experiment with a wide range of word embeddings and evaluate on one general and two scientific relation classification datasets. We release all of our code for relation classification and data for scientific word embeddings to enable the reproduction of our experiments.