A Sequence Learning Method for Domain-Specific Entity Linking
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Assoc Computational Linguistics-Acl
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. in this paper, we address these problems by introducing a two-fold neural model. First, we match easy mentionentity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.
Açıklama
7th Workshop on Named Entities (NEWS) -- JUL 20, 2018 -- Melbourne, AUSTRALIA
Anahtar Kelimeler
[No Keyword]
Kaynak
Named Entities
WoS Q Değeri
N/A
