Turney, P. (1999), `Learning to extract keyphrases from text', Technical Report ERB-1057, National Research Council, Institute for Information Technology.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....noun or gerund, and permits phrases such as programming by demonstration. Several browsing interfaces are based on keyphrases. Jones and Paynter [7] automatically insert hyperlinks into digital library collections using keyphrases as link anchors and document clusters as destinations. Turney [17] uses keyphrases to construct searchable subject indexes. Gutwin et al. 6] search for clusters of documents that share keyphrases. Phrases in the result list can be reused as search terms, allowing the user to search increasingly specific variations on a phrase. Price et al. 13] allow readers to ....

Turney, P.D. (1999) Learning to Extract Keyphrases from Text. NRC Technical Report ERB-1057, National Research Council, Canada.

....described here, employs linguistic and information retrieval techniques to extract phrases from a new document that are likely to characterize it. The training set is used to tune the parameters of the extraction algorithm, and any phrase in the new document is a potential keyphrase. Turney [29] describes a system for keyphrase extraction, GenEx, that is based on a set of parametrized heuristic rules which are fine tuned using a genetic algorithm. The genetic algorithm optimizes the number of correctly identified keyphrases in the training documents by adjusting the rules parameters. ....

....We have evaluated this keyphrase extraction method on several different document collections with author assigned keyphrases. The criterion for success is the extent to which the algorithm produces the same stemmed phrases as authors do. This method of evaluation is the same as used by Turney [29], and on comparing our results with GenEx we conclude that both methods perform at about the same level. An interesting question is how keyphrase extraction performance scales with the amount of training data available. There are two ways in which the quantity of available documents can influence ....

Turney, P. (in press) "Learning to extract keyphrases from text." Information Retrieval.

....to produce the best extracted summaries. To increase flexibility in the configurability of the system, we would ideally have a number of different modules that could be plugged in at the appropriate points in the greater system. For keyphrase extraction, we are using Peter Turney s Extractor [10, 11]. As an alternative we decided to build a simple keyphrase extractor in house as well. The goal was to keep the extractor simple and to apply any linguistic insight we might have to the process. This paper presents our simple keyphrase extractor (herein referred to as B C for lack of a better ....

Turney, Peter D. (1999). "Learning to Extract Keyphrases from Text." National Research Council, Institute for Information Technology, Technical Report ERB-1057.

....relevant users to which to route itself, hence its importance. Given this importance, we are attempting to automate the process of fourth draft.tex; 17 08 2001; 13:51; p. 12 ACORN 13 keyphrase discovery, at least for textual documents, and are able to use automated techniques such as Extractor [28]. However, there remains no adequate method of attaining keyphrases from, for example, music les or images, thus we require the user to do some additional work. Our hope is that this work is not too onerous considering the fact that the more keyphrases, and the more accurate they are, the better ....

....are classi ed into ve categories based on their content: Category 1 (articles 1 11) Category 2 (articles 12 22) Category 3 (articles 23 32) Category 4 (articles 33 42) and Category 5 (articles 43 49) Each category represents a speci c topic. The text document indexing software Extractor [28] is used to obtain keyphrases and their weights. The details of these articles and the output obtained using Extractor are given in [32] One article from each category was selected and the similarities between this article and the entire set of articles from all categories were calculated using ....

Turney, P.: 1999, `Learning to Extract Keyphrases from Text'. Technical Report ERB-1057, National Research Council Canada, Institute for Information Technology, Ottawa, Canada. http://extractor.iit.nrc.ca/.

....closed queries. They build a discrete HMM to extract information, each state representing a particular label. Wermter et al. 1999) use recurrent neural networks which are another formalism for sequence modeling for routing. Text segmentation has also been considered from an IR perspective. (Turney, 1999) uses decision trees for the extraction of keywords or keyphrases (two or more words) from text, framing the extraction problem as a classification problem on pre segmented text. A large amount of work has also been dedicated to text segmentation into coherent passages, e.g. Hearst Plaunt, ....

Turney P. D., (1999), Learning to extract keyphrases from text. Technical Report ERB-1057, National Research Council, Institute for Information Technology.

....gerund, and permits phrases such as programming by demonstration. Several browsing interfaces are based on keyphrases. Jones and Paynter [7] automatically insert hyperlinks into digital library collections using keyphrases as link anchors and document clusters as destinations. Martin and Turney [15] use keyphrases to construct searchable subject indexes. Gutwin et al. 6] search for clusters of documents that share keyphrases. Phrases in the result list can be reused as search terms, allowing the user to search increasingly specific variations on a phrase. All three interfaces treat phrases ....

Turney, P.D. (1999) "Learning to Extract Keyphrases from Text." NRC Technical Report ERB-1057, National Research Council, Canada.

.... 1998, Budzik et al. 1998, Kulyukin 1999, Budzik and Hammond 2000, Rhodes 2000, Maglio 2000) Designers of such systems typically make the assumption that the goal of the system should be to retrieve objects that are similar to the one currently being manipulated (e.g. to find more like this (Turney 1999)) This is motivated by the underlying vector space model of Copyright 2000, American Association for Artificial Intelligence (www.aaai.org) All rights reserved. information retrieval (Salton, Wong, and Yang 1971) typically used by such systems. In the vector space model, requests are matched ....

Turney, P. (1999). Learning to Extract Keyphrases from Text, Tech. Report Number NRC-41622, National Research Council Canada, Institute for Information Technology.

....We apply techniques from machine learning to the problem of automatically extracting keyphrases 1 Official Mark of the Government of Canada. A demonstration version is available from http: extractor.iit.nrc.ca . from text. We approach the problem as a supervised learning task. Another paper (Turney, 1999) describes our algorithm for keyphrase extraction with English text. In this paper, we focus on the implementation of an efficient and effective way to extract keyphrases from Japanese text. This paper begins with a brief introduction to Extractor. Then it explains the main characteristics of ....

....noun phrases of the text, in a canonical form, and we start extracting the essence of the text. A usual way to do this in English is to remove the inflexion of the words, namely stemming. There are some stemming algorithms available for English. Lovins (1968) and Porter (1980) are good examples. Turney (1999) shows that, for a keyphrase extraction algorithm, a sophisticated stemmer might not be necessary. Since English stemmers cannot be applied to Japanese, we usually need help from a morphological analyser to segment the compound words (e.g. into their component words (e.g. However, ....

Turney, P.D. (1999). Learning to Extract Keyphrases from Text, NRC Technical Report ERB1057, National Research Council Canada.

....seen in the training data. Keyphrase extraction, the approach used here, does not use a controlled vocabulary, but instead chooses keyphrases from the text itself. It employs lexical and information retrieval techniques to extract phrases from the document text that are likely to characterize it [12]. In this approach, the training data is used to tune the parameters of the extraction algorithm. This paper describes a new keyphrase extraction algorithm, Kea, that is simple and effective, and performs at the current state of the art [5] It uses the Na ve Bayes machine learning algorithm for ....

Turney, P. (1999) "Learning to extract keyphrases from text." Submitted to J Information Retrieval.

....of possible keyphrases to a selected vocabulary. On the contrary, any phrase in a new document can be identified extracted as a keyphrase. Using a set of training documents, machine learning is used to determine which properties distinguish phrases that are keyphrases from ones that are not. Turney [ 1999 ] describes a system for keyphrase extraction, GenEx, based on a set of parametrized heuristic rules that are fine tuned using a genetic algorithm. The genetic algorithm optimizes the number of correctly identified keyphrases in the training documents by adjusting the rules parameters. Turney ....

....document collections with author assigned keyphrases. Our cri 2 The naive Bayes implementation used by Kea initializes all counts to one. terion for success is the extent to which Kea produces the same stemmed phrases as authors do. 3 Because this method of evaluation is the same as used by Turney [ 1999 ] we can directly compare Kea s performance to his results. Comparison to GenEx We compared Kea and GenEx using two experimental settings from Turney s paper. 4 The first one involves training and testing on journal articles. In this setting, 55 articles are used for training (6 from the ....

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P.D. Turney. Learning to extract keyphrases from text. Technical Report ERB-1057, National Research Council, Institute for Information Technology, 1999.

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Turney, P.D. Learning to Extract Keyphrases from Text. National Research Council, Institute for Information Technology, Technical Report ERB-1057, 1999.

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Turney, P. (1999), `Learning to extract keyphrases from text', Technical Report ERB-1057, National Research Council, Institute for Information Technology.

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Turney, P. D., "Learning to Extract Keyphrases from Text", NRC Technical Report ERB-1057, National Research Council Canada, 1999.

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Turney, P.D. Learning to Extract Keyphrases from Text. Technical Report ERB-1057 (NRC #41622). Canadian National Research Council, Institute for Information Technology, 1999.

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Turney, P., Learning to extract key phrases from text, Technical Report ERB-1057, National Research Council, Institute for Information Technology, 1999.

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