Combining entity co-occurrence information and sentence semantic features for relation extraction
نویسندگان
چکیده
منابع مشابه
Entity-Focused Sentence Simplification for Relation Extraction
Relations between entities in text have been widely researched in the natural language processing and informationextraction communities. The region connecting a pair of entities (in a parsed sentence) is often used to construct kernels or feature vectors that can recognize and extract interesting relations. Such regions are useful, but they can also incorporate unnecessary distracting informati...
متن کاملChinese Entity Relation Extraction Based on Word Co-occurrence
Chinese entity relation extraction is a part of entity relation extraction. According to entity relation extraction technology and the features of Chinese news corpus, this paper proposes a novel method for Chinese entities relation extraction. The method, named WCORE (word co-occurrence relation extraction), first measures the semantic similarity by word co-occurrence and then adopts pattern m...
متن کاملCombining a Co-occurrence-Based and a Semantic Measure for Entity Linking
One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens...
متن کاملPeople Summarization by Combining Named Entity Recognition and Relation Extraction
The two most important tasks in entity information summarization from the Web are named entity recognition and relation extraction. Little work has been done toward an integrated statistical model for understanding both named entities and their relationships. Most of the previous works on relation extraction assume the named entities are pre-given. The drawbacks of these sequential models are t...
متن کاملCombining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction
Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general ap...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SCIENTIA SINICA Informationis
سال: 2018
ISSN: 1674-7267
DOI: 10.1360/n112018-00157