태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Taxonomy learning is the task of hierarchically classifying concepts in an automatic manner from text corpora. The process of building taxonomies is usually divided into two main steps: (1) extracting hypernyms for concepts, which may constitute a field of research in itself (see Hypernym Discovery below) and (2) refining the structure into a taxonomy.
Description from NLP Progress
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The SemEval-2018 hypernym discovery evaluation benchmark (Camacho-Collados et al. 2018) contains three domains (general, medical and music) and is also available in Italian and Spanish (not in this repository). For each domain a target corpus and vocabulary (i.e. hypernym search space) are provided. The dataset contains both concepts (e.g. dog) and entities (e.g. Manchester United) up to trigrams. Source: NLP Progress Image source: SemEval-2018 Task 9: Hypernym Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.