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191
BabelNet: The automatic construction, evaluation and application of a . . .
- ARTIFICIAL INTELLIGENCE
, 2012
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Probase: A Probabilistic Taxonomy for Text Understanding
"... Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing onto ..."
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Cited by 76 (21 self)
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Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing ontologies has the needed depth and breadth for “universal understanding”. In this paper, we present a universal, probabilistic taxonomy that is more comprehensive than any existing ones. It contains 2.7 million concepts harnessed automatically from a corpus of 1.68 billion web pages. Unlike traditional taxonomies that treat knowledge as black and white, it uses probabilities to model inconsistent, ambiguous and uncertain information it contains. We present details of how the taxonomy is constructed, its probabilistic modeling, and its potential applications in text understanding.
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
- IEEE TPAMI
"... Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the ..."
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Cited by 61 (14 self)
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Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most “important ” node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets, and show that our graph-based approach performs comparably to the state of the art.
A Survey of Automatic Query Expansion in Information Retrieval
"... The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user’s original que ..."
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Cited by 55 (2 self)
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The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user’s original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed: Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)?
Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia
"... We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category d ..."
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Cited by 39 (3 self)
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We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a wellstructured top-level taxonomy from WordNet. 1
Efficient Approximate Entity Extraction with Edit Distance Constraints
"... Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extrac ..."
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Cited by 36 (8 self)
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Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance. In this paper, we study the problem of approximate dictionary matching with edit distance constraints. Compared to existing studies using token-based similarity constraints, our problem definition enables us to capture typographical or orthographical errors, both of which are common in entity extraction tasks yet may be missed by token-based similarity constraints. Our problem is technically challenging as existing approaches based on q-gram filtering have poor performance due to the existence of many short entities in the dictionary. Our proposed solution is based on an improved neighborhood generation method employing novel partitioning and prefix pruning techniques. We also propose an efficient document processing algorithm that minimizes unnecessary comparisons and enumerations and hence achieves good scalability. We have conducted extensive experiments on several publicly available named entity recognition datasets. The proposed algorithm outperforms alternative approaches by up to an order of magnitude.
Contextual analysis of word meanings in type-theoretical semantics
- In Logical aspects of computational linguistics (LACL’2011). LNAI 6736
, 2011
"... Abstract. Word meanings are context sensitive and may change in different situations. In this paper, we consider how contexts and the associated contextual meanings of words may be represented in typetheoretical semantics, the formal semantics based on modern type theories.Itisshown,inparticular,tha ..."
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Cited by 25 (8 self)
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Abstract. Word meanings are context sensitive and may change in different situations. In this paper, we consider how contexts and the associated contextual meanings of words may be represented in typetheoretical semantics, the formal semantics based on modern type theories.Itisshown,inparticular,thatthe framework of coercive subtyping provides various useful tools in the representation. 1
Natural Language Questions for the Web of Data
"... The Linked Data initiative comprises structured databases in the Semantic-Web data model RDF. Exploring this heterogeneous data by structured query languages is tedious and error-prone even for skilled users. To ease the task, this paper presents a methodology for translating natural language questi ..."
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Cited by 24 (3 self)
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The Linked Data initiative comprises structured databases in the Semantic-Web data model RDF. Exploring this heterogeneous data by structured query languages is tedious and error-prone even for skilled users. To ease the task, this paper presents a methodology for translating natural language questions into structured SPARQL queries over linked-data sources. Our method is based on an integer linear program to solve several disambiguation tasks jointly: the segmentation of questions into phrases; the mapping of phrases to semantic entities, classes, and relations; and the construction of SPARQL triple patterns. Our solution harnesses the rich type system provided by knowledge bases in the web of linked data, to constrain our semantic-coherence objective function. We present experiments on both the question translation and the resulting query answering.
The communicative function of ambiguity in language
- Cognition
, 2012
"... a b s t r a c t We present a general information-theoretic argument that all efficient communication systems will be ambiguous, assuming that context is informative about meaning. We also argue that ambiguity allows for greater ease of processing by permitting efficient linguistic units to be re-us ..."
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Cited by 24 (4 self)
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a b s t r a c t We present a general information-theoretic argument that all efficient communication systems will be ambiguous, assuming that context is informative about meaning. We also argue that ambiguity allows for greater ease of processing by permitting efficient linguistic units to be re-used. We test predictions of this theory in English, German, and Dutch. Our results and theoretical analysis suggest that ambiguity is a functional property of language that allows for greater communicative efficiency. This provides theoretical and empirical arguments against recent suggestions that core features of linguistic systems are not designed for communication.
Taxonomy induction based on a collaboratively built knowledge repository.
- Artif. Intell.
, 2011
"... The category system in Wikipedia can be taken as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexico-syntactic matching. The result is a large scale taxonomy. For evaluation we propose a method which (1) manually det ..."
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Cited by 21 (2 self)
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The category system in Wikipedia can be taken as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexico-syntactic matching. The result is a large scale taxonomy. For evaluation we propose a method which (1) manually determines the quality of our taxonomy, and (2) automatically compares its coverage with ResearchCyc, one of the largest manually created ontologies, and the lexical database WordNet. Additionally, we perform an extrinsic evaluation by computing semantic similarity between words in benchmarking datasets. The results show that the taxonomy compares favorably in quality and coverage with broadcoverage manually created resources.
