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95
BOpen information extraction using Wikipedia
- in Proc. 48th Annu. Meeting Assoc. Comput. Linguist., 2010
"... Information-extraction (IE) systems seek to distill semantic relations from naturallanguage text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the ..."
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Cited by 96 (3 self)
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Information-extraction (IE) systems seek to distill semantic relations from naturallanguage text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform? This paper presents WOE, an open IE system which improves dramatically on TextRunner’s precision and recall. The key to WOE’s performance is a novel form of self-supervised learning for open extractors — using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Like TextRunner, WOE’s extractor eschews lexicalized features and handles an unbounded set of semantic relations. WOE can operate in two modes: when restricted to POS tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher. 1
Information extraction
- FnT Databases
"... The automatic extraction of information from unstructured sources has opened up new avenues for querying, organizing, and analyzing data by drawing upon the clean semantics of structured databases and the abundance of unstructured data. The field of information extraction has its genesis in the natu ..."
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Cited by 95 (4 self)
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The automatic extraction of information from unstructured sources has opened up new avenues for querying, organizing, and analyzing data by drawing upon the clean semantics of structured databases and the abundance of unstructured data. The field of information extraction has its genesis in the natural language processing community where the primary impetus came from competitions centered around the recognition of named entities like people names and organization from news articles. As society became more data oriented with easy online access to both structured and unstructured data, new applications of structure extraction came around. Now, there is interest in converting our personal desktops to structured databases, the knowledge in scientific publications to structured records, and harnessing the Internet for structured fact finding queries. Consequently, there are many different communities of researchers bringing in techniques from machine learning, databases, information retrieval, and computational linguistics for various aspects of the information extraction problem. This review is a survey of information extraction research of over two decades from these diverse communities. We create a taxonomy of the field along various dimensions derived from the nature of theextraction task, the techniques used for extraction, the variety of input resources exploited, and the type of output produced. We elaborate on rule-based and statistical methods for entity and relationship extraction. In each case we highlight the different kinds of models for capturing the diversity of clues driving the recognition process and the algorithms for training and efficiently deploying the models. We survey techniques for optimizing the various steps in an information extraction pipeline, adapting to dynamic data, integrating with existing entities and handling uncertainty in the extraction process. 1
A systematic exploration of the feature space for relation extraction
- In HLT/NAACL
, 2007
"... Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In ..."
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Cited by 44 (2 self)
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Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and evaluate the effectiveness of different feature subspaces. We present a general definition of feature spaces based on a graphic representation of relation instances, and explore three different representations of relation instances and features of different complexities within this framework. Our experiments show that using only basic unit features is generally sufficient to achieve state-of-the-art performance, while overinclusion of complex features may hurt the performance. A combination of features of different levels of complexity and from different sentence representations, coupled with task-oriented feature pruning, gives the best performance. 1
Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
- CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING
, 2009
"... This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such ..."
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Cited by 31 (13 self)
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This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such representations in the automated relation extraction from texts. We process the above data by means of Support Vector Machines along with the syntactic tree, the partial tree and the word sequence kernels. Our study on the ACE 2004 corpus illustrates that the combination of the above kernels achieves high effectiveness and significantly improves the current state-of-the-art. 1
Extraction of semantic biomedical relations from text using conditional random fields
, 2008
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Semi-supervised relation extraction with large-scale word clustering
- In Proceedings of the
"... We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When ..."
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Cited by 21 (4 self)
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We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed a state-of-the-art supervised baseline system. 1
A Re-examination of Dependency Path Kernels for Relation Extraction
"... Extracting semantic relations between entities from natural language text is an important step towards automatic knowledge extraction from large text collections and the Web. The state-of-the-art approach to relation extraction employs Support Vector Machines (SVM) and kernel methods for classificat ..."
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Cited by 16 (0 self)
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Extracting semantic relations between entities from natural language text is an important step towards automatic knowledge extraction from large text collections and the Web. The state-of-the-art approach to relation extraction employs Support Vector Machines (SVM) and kernel methods for classification. Despite the diversity of kernels and the near exhaustive trial-and-error on kernel combination, there lacks a clear understanding of how these kernels relate to each other and why some are superior than others. In this paper, we provide an analysis of the relative strength and weakness of several kernels through systematic experimentation. We show that relation extraction can benefit from increasing the feature space through convolution kernel and introducing bias towards more syntactically meaningful feature space. Based on our analysis, we propose a new convolution dependency path kernel that combines the above two benefits. Our experimental results on the standard ACE 2003 datasets demonstrate that our new kernel gives consistent and significantly better performance than baseline methods, obtaining very competitive results to the state-of-the-art performance.
Construction of Predicate-argument Structure Patterns for Biomedical Information Extraction
- In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
, 2006
"... This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical variants, patterns on PASs are expected to be more ..."
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Cited by 11 (0 self)
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This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical variants, patterns on PASs are expected to be more generalized than those on surface words. In addition, patterns are divided into components to improve recall and we introduce a Support Vector Machine to learn a prediction model using pattern matching results. In this paper, we present experimental results and analyze them on how well protein-protein interactions were extracted from MEDLINE abstracts. The results demonstrated that our method improved accuracy compared to a machine learning approach using surface word/part-of-speech patterns. 1
A hybrid approach for extracting semantic relations from texts
- in Proceedings of 2nd Workshop on Ontology Learning and Population
, 2006
"... We present an approach for extracting relations from texts that exploits linguistic and empirical strategies, by means of a pipeline method involving a parser, partof-speech tagger, named entity recognition system, pattern-based classification and word sense disambiguation models, and resources such ..."
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Cited by 9 (0 self)
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We present an approach for extracting relations from texts that exploits linguistic and empirical strategies, by means of a pipeline method involving a parser, partof-speech tagger, named entity recognition system, pattern-based classification and word sense disambiguation models, and resources such as ontology, knowledge base and lexical databases. The relations extracted can be used for various tasks, including semantic web annotation and ontology learning. We suggest that the use of knowledge intensive strategies to process the input text and corpusbased techniques to deal with unpredicted cases and ambiguity problems allows to accurately discover the relevant relations between pairs of entities in that text. 1
