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| Content Provider | ACM Digital Library |
|---|---|
| Author | El-Kishky, Ahmed Ren, Xiang Han, Jiawei Ji, Heng |
| Abstract | In today's computerized and information-based society, individuals are constantly presented with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To extract value from these large, multi-domain pools of text, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their fine-grained types (e.g., people, product and food) in a scalable way. Since these methods do not rely on annotated data, predefined typing schema or hand-crafted features, they can be quickly adapted to a new domain, genre and language. We demonstrate on real datasets including various genres (e.g., news articles, discussion forum posts, and tweets), domains (general vs. bio-medical domains) and languages (e.g., English, Chinese, Arabic, and even low-resource languages like Hausa and Yoruba) how these typed entities aid in knowledge discovery and management. |
| Starting Page | 2235 |
| Ending Page | 2239 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450335317 |
| DOI | 10.1145/2882903.2912567 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text mining Entity Entity recognition Typing Phrases Phrase mining Entity typing |
| Content Type | Text |
| Resource Type | Article |
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