Relation extraction and the influence of automatic named-entity recognition

Abstract

We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We present the results of experiments on extracting five different types of relations from a dataset of newswire documents and show that each information source provides a useful contribution to the recognition task. Usually the combined kernel significantly increases the precision with respect to the basic kernels, sometimes at the cost of a slightly lower recall. Moreover, we performed a set of experiments to assess the influence of the accuracy of named-entity recognition on the performance of the relation-extraction algorithm. Such experiments were performed using both the correct named entities (i.e., those manually annotated in the corpus) and the noisy named entities (i.e., those produced by a machine learning-based named-entity recognizer). The results show that our approach significantly improves the previous results obtained on the same dataset.

A Comparative Study on Statistical Classification Methods in Relation Extraction

“…Miller et al adapted a probabilistic context-free parser for information extraction [Miller et al, 2000]. Giuliano et al thoroughly evaluated the effect of NER on RE [Giuliano et al, 2007]. Alicante and Corazza proposed barrier features, such as N-Grams of POS (Part of Speech), word prefixes and suffixes, hypernyms from WordNet etc.…”

“…The Roth and Yih data set is not divided in training and test set. Therefore assessment is performed by following the 5-fold cross validation protocol, as in [Giuliano et al, 2007] [Roth & Yih, 2007] [Kate & Mooney, 2010].…”

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

“…In the past, several works [2], [3], [17], [18], [21] focused on how to identify relevant entities (e.g., person, location and organization) and their relations (e.g., work for, live in and kill) such as work for (person-organization), live in (personlocation) and kill (person-person). In contrast to these previous works, this work focuses on predicate-oriented relations among relevant entities (e.g., person, location and action) and their relationships (e.g., related and unrelated) are identified such as related (person-action), unrelated (personaction), related (action-person), unrelated (action-person), related (action-location) and unrelated (action-location).…”

“…Towards discovery of relations among NEs, named entity extraction as well as other preprocesses such as tokenization, sentence splitting, part-of-speech tagging and lemmatization, are usually applied [18]- [21]. As an early work on relation extraction, Ferrández et al [4] extracted relations based on clause splitting of documents.…”

“…Interaction, movement, transfer, creation and destruction events were five types of events identified and characterized in the Event Detection and Characterization (EDC) task. In previous works [2], [3], [17], [18], [21], the detected NE relations are mostly of specific actions, such as work for (person-organization), live in (person-location) and kill (person-person). NE extraction or NE relation extraction from texts in English and other languages with sufficient processing tools, syntactic information can be provided to analyze the sentence structure and extract NEs, and semantic information is usually applied to detect relations among such NEs.…”