[NLP] FastText 관련 참고문헌 정리

Enriching Word Vectors with Subword Information

• RISING FASTBALL - [자연어처리][paper review] FastText: Enriching Word Vectors with Subword Information
• HONG YP's Data Science BLOG - [논문 스터디] FastText: Enriching Word Vectors with Subword Information

FASTTEXT.ZIP: COMPRESSING TEXT CLASSIFICATION MODELS

Fast Linear Model for Knowledge Graph Embeddings

Linear models (Joachims, 1998) are powerful and efficient baselines for text classification. In particular, the fastText model proposed by Joulin et al. (2017) achieves state-of-the-art performance on many datasets by combining several standard tricks, such as low rank constraints (Schutze, 1992) and n-gram features (Wang and Manning, 2012). The same approach can be applied to any problem where the input is a set of discrete tokens. For example, a KB is composed of entities (or nodes) and relations (or edges) that can be represented by a unique discrete token.

Multilingual Constituency Parsing with Self-Attention and Pre-Training