Results 1 - 10
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262
Unsupervised namedentity extraction from the web: An experimental study.
- Artificial Intelligence,
, 2005
"... Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture and ..."
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Cited by 372 (39 self)
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Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KNOW-ITALL extracted over 50,000 class instances, but suggested a challenge: How can we improve KNOWITALL's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., "chemist" and "biologist" are identified as sub-classes of "scientist"). List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.
From frequency to meaning : Vector space models of semantics
- Journal of Artificial Intelligence Research
, 2010
"... Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are begi ..."
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Cited by 347 (3 self)
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Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term–document, word–context, and pair–pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field. 1.
Measuring praise and criticism: Inference of semantic orientation from association
- ACM Transactions on Information Systems
, 2003
"... The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., “honest”, “intrepid”) and negative semantic orientation indicates criticism (e.g., “disturbing”, “superfluous”). Semantic orientation varies in both direction (positive or neg ..."
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Cited by 311 (6 self)
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The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., “honest”, “intrepid”) and negative semantic orientation indicates criticism (e.g., “disturbing”, “superfluous”). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8 % on the full test set, but the accuracy rises above 95 % when the algorithm is allowed to abstain from classifying mild words.
Dependency-based construction of semantic space models
- Computational Linguistics
, 2007
"... Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of m ..."
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Cited by 236 (14 self)
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Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive science and natural language processing: semantic priming, synonymy detection, and word sense disambiguation. In all cases, our framework obtains results that are comparable or superior to the state of the art. 1.
Wikirelate! computing semantic relatedness using wikipedia
- In Proceedings of the 21st national conference on Artificial intelligence
, 2006
"... Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datase ..."
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Cited by 191 (3 self)
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Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.
Corpus-based and knowledge-based measures of text semantic similarity
- IN IPROCEEDINGS OF THE 21ST NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE - VOLUME 1
, 2006
"... This paper presents a method for measuring the semantic similarity of texts, using corpus-based and knowledge-based measures of similarity. Previous work on this problem has focused mainly on either large documents (e.g. text classification, information retrieval) or individual words (e.g. synonymy ..."
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Cited by 151 (4 self)
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This paper presents a method for measuring the semantic similarity of texts, using corpus-based and knowledge-based measures of similarity. Previous work on this problem has focused mainly on either large documents (e.g. text classification, information retrieval) or individual words (e.g. synonymy tests). Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientific documents, imagine captions, product descriptions), in this paper we focus on measuring the semantic similarity of short texts. Through experiments performed on a paraphrase data set, we show that the semantic similarity method outperforms methods based on simple lexical matching, resulting in up to 13 % error rate reduction with respect to the traditional vector-based similarity metric.
Web-scale information extraction in knowItAll: (preliminary results).
- In Proceedings of the 13th International Conference on World Wide Web (WWW ’04),
, 2004
"... ABSTRACT Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, assess confidence, or fuse informa ..."
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Cited by 151 (5 self)
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ABSTRACT Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, assess confidence, or fuse information from multiple documents. This paper introduces KNOWITALL, a system that aims to automate the tedious process of extracting large collections of facts from the web in an autonomous, domain-independent, and scalable manner. The paper describes preliminary experiments in which an instance of KNOWITALL, running for four days on a single machine, was able to automatically extract 54,753 facts. KNOWITALL associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KNOWITALL's architecture and reports on lessons learned for the design of large-scale information extraction systems.
From Distributional to Semantic Similarity
, 2003
"... Lexical-semantic resources, including thesauri and WORDNET, have been successfully incorporated into a wide range of applications in Natural Language Processing. However they are very difficult and expensive to create and maintain, and their usefulness has been severely hampered by their limited cov ..."
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Cited by 145 (11 self)
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Lexical-semantic resources, including thesauri and WORDNET, have been successfully incorporated into a wide range of applications in Natural Language Processing. However they are very difficult and expensive to create and maintain, and their usefulness has been severely hampered by their limited coverage, bias and inconsistency. Automated and semi-automated methods for developing such resources are therefore crucial for further resource development and improved application performance.
Measuring semantic similarity between words using web search engines
- in Proceedings of WWW
, 2007
"... Measuring the semantic similarity between words is an important component in various semantic web-related applications such as community mining, relation extraction and au-tomatic meta data extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring ..."
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Cited by 138 (7 self)
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Measuring the semantic similarity between words is an important component in various semantic web-related applications such as community mining, relation extraction and au-tomatic meta data extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or enti-ties) remains a challenging task. We propose a semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The pro-posed method exploits page counts and text snippets returned by a Web search engine. We define various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexical-syntactic patterns from text snippets. These different similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similar-ity measures by a wide margin, achieving a correlation coefficient of 0.867. Moreover, the proposed semantic similarity measure significantly improves the accuracy (F-measure of 0.78) in a community mining task, and in an entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content. Key words: semantic similarity measures, web mining, community extraction
Extracting semantic representations from word co-occurrence statistics: A computational study
- Behavior Research Methods
, 2007
"... Abstract: In a previous paper we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of Pointwise Mutual Information (PMI) values from very small co-occurr ..."
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Cited by 128 (2 self)
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Abstract: In a previous paper we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of Pointwise Mutual Information (PMI) values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This paper extends that study by investigating the use of three further factors, namely the application of stop-lists, word stemming, and dimensionality reduction using Singular Value Decomposition (SVD), that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.
