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406
A Survey of Web Information Extraction Systems
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2006
"... The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-fr ..."
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Cited by 171 (3 self)
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The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches.
Unsupervised Personal Name Disambiguation
- Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003
, 2003
"... This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a languag ..."
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Cited by 161 (4 self)
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This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping process. The induced clustering of named entities are then partitioned and linked to their real referents via the automatically extracted biographic data. Performance is evaluated based on both a test set of handlabeled multi-referent personal names and via automatically generated pseudonames.
YAGO: A Large Ontology from Wikipedia and WordNet
, 2008
"... This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy a ..."
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Cited by 148 (16 self)
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This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.
Empirical Methods in Information Extraction
- AI magazine
, 1997
"... this article surveys the use of empirical methods for a particular natural language understanding task that is inherently domain-specific. The task is information extraction. Very generally, an information extraction system takes as input an unrestricted text and "summarizes" the text with ..."
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Cited by 123 (7 self)
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this article surveys the use of empirical methods for a particular natural language understanding task that is inherently domain-specific. The task is information extraction. Very generally, an information extraction system takes as input an unrestricted text and "summarizes" the text with respect to a prespecified topic or domain of interest: it finds useful information about the domain and encodes that information in a structured form, suitable for populating databases. In contrast to in-depth natural language understanding tasks, information extraction systems effectively skim a text to find relevant sections and then focus only on these sections in subsequent processing. The information extraction system in Figure 1, for example, summarizes stories about natural disasters, extracting for each such event the type of disaster, the date and time that it occurred, and data on any property damage or human injury caused by the event. Infor
Extraction Patterns for Information Extraction Tasks: A Survey
, 1999
"... Information Extraction systems rely on a set of extraction patterns that they use in order to retrieve from each docu-ment the relevant information. In this paper we survey the various types of extraction patterns that are generated by ma-chine learning algorithms. We identify three main categories ..."
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Cited by 118 (0 self)
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Information Extraction systems rely on a set of extraction patterns that they use in order to retrieve from each docu-ment the relevant information. In this paper we survey the various types of extraction patterns that are generated by ma-chine learning algorithms. We identify three main categories of patterns, which cover a variety of application domains, and we compare and contrast the patterns from each category.
Comparative Experiments on Learning Information Extractors for Proteins and their Interactions
, 2004
"... Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in M ..."
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Cited by 106 (7 self)
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Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction eorts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. We demonstrate that machine learning approaches using support vector machines and maximum entropy are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules.
Multistrategy Learning for Information Extraction
- In Proceedings of the Fifteenth International Conference on Machine Learning
, 1998
"... Information extraction (IE) is the problem of filling out pre-defined structured summaries from text documents. We are interested in performing IE in non-traditional domains, where much of the text is often ungrammatical, such as electronic bulletin board posts and Web pages. We suggest that the bes ..."
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Cited by 99 (3 self)
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Information extraction (IE) is the problem of filling out pre-defined structured summaries from text documents. We are interested in performing IE in non-traditional domains, where much of the text is often ungrammatical, such as electronic bulletin board posts and Web pages. We suggest that the best approach is one that takes into account many different kinds of information, and argue for the suitability of a multistrategy approach. We describe learners for IE drawn from three separate machine learning paradigms: rote memorization, term-space text classification, and relational rule induction. By building regression models mapping from learner confidence to probability of correctness and combining probabilities appropriately, it is possible to improve extraction accuracy over that achieved by any individual learner. We describe three different multistrategy approaches. Experiments on two IE domains, a collection of electronic seminar announcements from a university computer science de...
Exploiting dictionaries in named entity extraction: Combining semi-markov extraction processes and data integration method
- In Proceedings of the ACM SIGKDD Conference
, 2004
"... We consider the problem of improving named entity recognition (NER) systems by using external dictionaries—more specifically, the problem of extending state-of-the-art NER systems by incorporating information about the similarity of extracted entities to entities in an external dictionary. This is d ..."
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Cited by 98 (6 self)
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We consider the problem of improving named entity recognition (NER) systems by using external dictionaries—more specifically, the problem of extending state-of-the-art NER systems by incorporating information about the similarity of extracted entities to entities in an external dictionary. This is difficult because most high-performance named entity recognition systems operate by sequentially classifying words as to whether or not they participate in an entity name; however, the most useful similarity measures score entire candidate names. To correct this mismatch we formalize a semi-Markov extraction process which relaxes the usual Markov assumptions. This process is based on sequentially classifying segments of several adjacent words, rather than single words. In addition to allowing a natural way of coupling NER and high-performance record linkage methods, this formalism also allows the direct use of other useful entity-level features, and provides a more natural formulation of the NER problem than sequential word classification. Experiments in multiple domains show that the new model can substantially improve extraction performance, relative to previously published methods for using external dictionaries in NER.
Automatic Segmentation of Text Into Structured Records
, 2001
"... In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the wareh ..."
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Cited by 95 (0 self)
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In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the warehouse address cleaning problem of transforming dirty addresses stored in large corporate databases as a single text field into subfields like "City" and "Street". Existing tools rely on hand-tuned, domain-specific rule-based systems.
Gimme’ The Context: Context-driven Automatic Semantic Annotation with C-PANKOW
, 2005
"... Without the proliferation of formal semantic annotations, the Semantic Web is certainly doomed to failure. In earlier work we presented a new paradigm to avoid this: the ’Self Annotating Web’, in which globally available knowledge is used to annotate resources such as web pages. In particular, we pr ..."
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Cited by 93 (3 self)
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Without the proliferation of formal semantic annotations, the Semantic Web is certainly doomed to failure. In earlier work we presented a new paradigm to avoid this: the ’Self Annotating Web’, in which globally available knowledge is used to annotate resources such as web pages. In particular, we presented a concrete method instantiating this paradigm, called PANKOW (Pattern-based ANnotation through Knowledge On the Web). In PANKOW, a named entity to be annotated is put into several linguistic patterns that convey competing semantic meanings. The patterns that are matched most often on the Web indicate the meaning of the named entity — leading to automatic or semi-automatic annotation. In this paper we present C-PANKOW (Context-driven PANKOW), which alleviates several shortcomings of PANKOW. First, by downloading abstracts and processing them off-line, we avoid the generation of large number of linguistic patterns and correspondingly large number of Google queries. Second, by linguistically analyzing and normalizing the downloaded abstracts, we increase the coverage of our pattern matching mechanism and overcome several limitations of the earlier pattern generation process. Third, we use the annotation context in order to distinguish the significance of a pattern match for the given annotation task. Our experiments show that C-PANKOW inherits all the advantages of PANKOW (no training required etc.), but in addition it is far more efficient and effective.
