Papers with Code 아카이브

Document Dating

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
2
보존된 평가 기록
6

Document Dating is the problem of automatically predicting the date of a document based on its content. Date of a document, also referred to as the Document Creation Time (DCT), is at the core of many important tasks, such as, information retrieval, temporal reasoning, text summarization, event detection, and analysis of historical text, among others.

For example, in the following document, the correct creation year is 1999. This can be inferred by the presence of terms 1995 and Four years after.

Swiss adopted that form of taxation in 1995. The concession was approved by the govt last September. Four years after, the IOC….

Description from NLP Progress

데이터셋

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

New York Times Annotated Corpus

The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

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