@inproceedings{sekine-dalwani-2010-ngram,
title = "Ngram Search Engine with Patterns Combining Token, {POS}, Chunk and {NE} Information",
author = "Sekine, Satoshi and
Dalwani, Kapil",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/158_Paper.pdf",
abstract = "We developed a search tool for ngrams extracted from a very large corpus (the current system uses the entire Wikipedia, which has 1.7 billion tokens). The tool supports queries with an arbitrary number of wildcards and/or specification by a combination of token, POS, chunk (such as NP, VP, PP) and Named Entity (NE). The previous system (Sekine 08) can only handle tokens and unrestricted wildcards in the query, such as * was established in *. However, being able to constrain the wildcards by POS, chunk or NE is quite useful to filter out noise. For example, the new system can search for NE=COMPANY was established in POS=CD. This finer specification reduces the number of outputs to less than half and avoids the ngrams which have a comma or a common noun at the first position or location information at the last position. It outputs the matched ngrams with their frequencies as well as all the contexts (i.e. sentences, KWIC lists and document ID information) where the matched ngrams occur in the corpus. It takes a fraction of a second for a search on a single CPU Linux-PC (1GB memory and 500GB disk) environment.",
}
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<abstract>We developed a search tool for ngrams extracted from a very large corpus (the current system uses the entire Wikipedia, which has 1.7 billion tokens). The tool supports queries with an arbitrary number of wildcards and/or specification by a combination of token, POS, chunk (such as NP, VP, PP) and Named Entity (NE). The previous system (Sekine 08) can only handle tokens and unrestricted wildcards in the query, such as * was established in *. However, being able to constrain the wildcards by POS, chunk or NE is quite useful to filter out noise. For example, the new system can search for NE=COMPANY was established in POS=CD. This finer specification reduces the number of outputs to less than half and avoids the ngrams which have a comma or a common noun at the first position or location information at the last position. It outputs the matched ngrams with their frequencies as well as all the contexts (i.e. sentences, KWIC lists and document ID information) where the matched ngrams occur in the corpus. It takes a fraction of a second for a search on a single CPU Linux-PC (1GB memory and 500GB disk) environment.</abstract>
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%0 Conference Proceedings
%T Ngram Search Engine with Patterns Combining Token, POS, Chunk and NE Information
%A Sekine, Satoshi
%A Dalwani, Kapil
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F sekine-dalwani-2010-ngram
%X We developed a search tool for ngrams extracted from a very large corpus (the current system uses the entire Wikipedia, which has 1.7 billion tokens). The tool supports queries with an arbitrary number of wildcards and/or specification by a combination of token, POS, chunk (such as NP, VP, PP) and Named Entity (NE). The previous system (Sekine 08) can only handle tokens and unrestricted wildcards in the query, such as * was established in *. However, being able to constrain the wildcards by POS, chunk or NE is quite useful to filter out noise. For example, the new system can search for NE=COMPANY was established in POS=CD. This finer specification reduces the number of outputs to less than half and avoids the ngrams which have a comma or a common noun at the first position or location information at the last position. It outputs the matched ngrams with their frequencies as well as all the contexts (i.e. sentences, KWIC lists and document ID information) where the matched ngrams occur in the corpus. It takes a fraction of a second for a search on a single CPU Linux-PC (1GB memory and 500GB disk) environment.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/158_Paper.pdf
Markdown (Informal)
[Ngram Search Engine with Patterns Combining Token, POS, Chunk and NE Information](http://www.lrec-conf.org/proceedings/lrec2010/pdf/158_Paper.pdf) (Sekine & Dalwani, LREC 2010)
ACL