Papers with Code 아카이브

Zero-shot Named Entity Recognition (NER)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 4
보존된 평가 기록: 10

Named Entity Recognition is Zero-shot Settings. The model has not been trained in the specific dataset

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

WikiEvents

WikiEvents is a document-level event extraction benchmark dataset which includes complete event and coreference annotation.

CrossNER is a cross-domain NER (Named Entity Recognition) dataset, a fully-labeled collection of NER data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains. Source: CrossNER

Broad Twitter Corpus

This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire.

HarveyNER

fine-grained location names extraction from disaster-related tweets

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.