Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The DAWT dataset consists of Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. Source: DAWT: Densely Annotated Wikipedia Texts across multiple languages
The dataset contains entities from IMDB, TheMovieDB and TheTVDB with goldstandard matches between the sources. Due to the licensing of IMDB we provide a script to build the IMDB part of the dataset yourself. The dataset contains a variety of entity types to match: persons, movies, series, episodes and companies.