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Hits 1 – 13 of 13
1
Construction of a sentimental word dictionary
Eduard C. Dragut
;
Clement Yu Prasad
;
Weiyi Meng
In: http://www.cs.binghamton.edu/~meng/pub.d/cikm785-dragut.pdf (2010)
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2
Recognition and classification of noun phrases in queries for effective retrieval
Wei Zhang
;
Shuang Liu
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/p711-zhang-cikm07a.pdf (2005)
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3
Word sense disambiguation in queries
Shuang Liu
;
Clement Yu
In: http://www.cs.binghamton.edu/~meng/pub.d/cikm05Shuang.pdf (2005)
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4
Recognition and classification of noun phrases in queries for effective retrieval
Wei Zhang
;
Shuang Liu
;
Clement Yu
...
In: http://www.cs.uic.edu/~wzhang/cikm2007/zhang_cikm_2007_recognition.pdf (2005)
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5
An effective approach to document retrieval via utilizing wordnet and recognizing phrases
Shuang Liu
;
Fang Liu
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/p266-liu.pdf (2004)
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6
An effective approach to document retrieval via utilizing wordnet and recognizing phrases
Shuang Liu
;
Fang Liu
;
Clement Yu
...
In: http://www.cs.uic.edu/~sliu/p096-liu.pdf (2004)
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7
Deriving Customized Integrated Web Query Interfaces
Eduard C. Dragut
;
Fang Fang
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/WI09.pdf
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8
Faceted Models of Blog Feeds
Lifeng Jia
;
Clement Yu
;
Weiyi Meng
In: http://www.cs.binghamton.edu/~meng/pub.d/p2279-jia-cikm13.pdf
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9
Abstract UIC at TREC-2004: Robust Track
Shuang Liu
;
Chaojing Sun
;
Clement Yu
In: http://trec.nist.gov/pubs/trec13/papers/uil-chicago.liu.robust.pdf
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10
UIC at TREC 2010 Faceted Blog Distillation
Lifeng Jia
;
Clement Yu
In: http://trec.nist.gov/pubs/trec19/papers/univ.illinois-chicago.blog.rev2.pdf
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11
Abstract UIC at TREC-2003: Robust Track (Draft)
Shuang Liu
;
Clement Yu
In: http://trec.nist.gov/pubs/trec12/papers/uillinois-chicago.robust.pdf
Abstract:
(UIC) participate in the robust track, which is a traditional ad hoc retrieval task. The emphasis is based on average effectiveness as well as individual topic effectiveness. Noun phrases in the query are identified and classified into 4 types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in a phrase are within a window of a certain size. The window sizes for different types of phrases are different. We consider phrases to be more important than individual terms. As a consequence, documents in response to a query are ranked with matching phrases given a higher priority. WordNet is used to disambiguate word senses and bring in useful synonyms and hyponyms once the correct senses of the words in a query have been identified. The usual pseudo-feedback process is modified so that the documents are also ranked according to phrase and word similarities with phrase matching having a higher priority. Five runs which use either title or title and description have been submitted. 1.
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.7481
http://trec.nist.gov/pubs/trec12/papers/uillinois-chicago.robust.pdf
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12
Topic Sentiment Change Analysis
Yu Jiang
;
Clement Yu
In: http://www.cs.binghamton.edu/~meng/pub.d/MLDM-camera_ready.pdf
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13
Polarity Consistency Checking for Domain Independent Sentiment Dictionaries
Eduard C. Dragut
;
Hong Wang
;
Prasad Sistla
...
In: http://www.cs.binghamton.edu/%7Emeng/pub.d/tkde-2015.pdf
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