1 |
Detecting Opinions and their Opinion Targets
|
|
|
|
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/07-NTCIR8-MOAT-ChoiY.pdf (2010)
|
|
BASE
|
|
Show details
|
|
2 |
Domain-specific sentiment analysis using contextual feature generation
|
|
|
|
In: http://ir.kaist.ac.kr/papers/2009/tsa09.pdf (2009)
|
|
BASE
|
|
Show details
|
|
3 |
Opinion Analysis based on Lexical Clues and their Expansion
|
|
|
|
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings6/NTCIR/53.pdf (2007)
|
|
BASE
|
|
Show details
|
|
4 |
Opinion Analysis based on Lexical Clues and their Expansion
|
|
|
|
In: http://ir.kaist.ac.kr/papers/2007/yhkim_opinion_final - NTCIR.pdf (2007)
|
|
BASE
|
|
Show details
|
|
5 |
Automatic Identification of Text Genres and Their Roles in SubjectBased Categorization
|
|
|
|
In: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf (2004)
|
|
Abstract:
Genre characterizes text differently than the usual subject or prepositional content that has been the focus of most information retrieval and classification research. We developed a new method for automatic genre classification that is based on statistically selected features obtained from both subject-classified and genre-classified training data. The main idea of the genre classification method is to calculate the weight of a feature for a genre class by using its frequency statistics for subject classifications. Having observed that the deviation formula and discrimination formula using document frequency ratios work as expected, we went on to study the roles of various types of features such as content-bearing words, function words, morphemes, and punctuations marks. In the first part of this paper, we present some of our findings in the roles of the feature types for genre classification, with a brief discussion of the genre-based classification. The genre classes we used are those often found in Web documents: accident reportages, newspaper editorials, personal homepages, product reviews, product specifications, research articles, and Q&A’s. The second part of the paper addresses the issue of how text genres help classifying documents based on the subject content of documents. This is a corollary to our original hypothesis that subject classification would help identifying the genre class of a document automatically. Our experimental work shows that while subject classes clearly help improving the genre-based classification, it is not clear whether using the genre class information for documents in the same way helps subject-based classification. However, we found that training a subject classifier with a set of documents belonging to a particular genre class improves subject-based classification. 1.
|
|
URL: http://csdl.computer.org/comp/proceedings/hicss/2004/2056/04/205640100b.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.1021
|
|
BASE
|
|
Hide details
|
|
6 |
Overview of clir task at the third ntcir workshop
|
|
|
|
In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2002a.pdf (2002)
|
|
BASE
|
|
Show details
|
|
7 |
Complementing Dictionary-Based Query Translations with Corpus Statistics for Cross-Language IR
|
|
|
|
In: http://www.mt-archive.info/MTS-1999-Myaeng.pdf (1999)
|
|
BASE
|
|
Show details
|
|
8 |
Using Mutual Information to Resolve Query Translation
|
|
|
|
In: http://acl.ldc.upenn.edu/P/P99/P99-1029.pdf (1999)
|
|
BASE
|
|
Show details
|
|
9 |
Using Syntactic Dependencies and WordNet Classes for Noun Event Recognition
|
|
|
|
In: http://ceur-ws.org/Vol-902/paper_5.pdf
|
|
BASE
|
|
Show details
|
|
10 |
Unsupervised word sense disambiguation using
|
|
|
|
In: http://ir.icu.ac.kr/papers/Unsupervised_Word_Sense_Disambiguation.pdf
|
|
BASE
|
|
Show details
|
|
11 |
Detecting experiences from weblogs
|
|
|
|
In: http://aclweb.org/anthology-new/P/P10/P10-1148.pdf
|
|
BASE
|
|
Show details
|
|
12 |
Concept Unification of Terms in Different Languages for IR
|
|
|
|
In: http://www.mt-archive.info/Coling-ACL-2006-Li.pdf
|
|
BASE
|
|
Show details
|
|
13 |
Concept Unification of Terms in Different Languages for IR
|
|
|
|
In: http://ir.kaist.ac.kr/papers/2006/Concept Unification of Terms in Different Languages for IR.pdf
|
|
BASE
|
|
Show details
|
|
14 |
Proceedingsof the Third NTCIR Workshop Overview of CLIR Task at the Third NTCIR Workshop
|
|
|
|
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-OV-CLIR-ChenK.rev.pdf
|
|
BASE
|
|
Show details
|
|
15 |
***Department of Statistics, Chungnam National University
|
|
|
|
In: http://nlg.csie.ntu.edu.tw/conference_papers/ntcir2005d.pdf
|
|
BASE
|
|
Show details
|
|
16 |
Generating and Mixing Feature Sets from Language Models for Sentiment Classification
|
|
|
|
In: http://ir.kaist.ac.kr/papers/2009/nlp-ke2009.pdf
|
|
BASE
|
|
Show details
|
|
17 |
Automatic Discovery of Technology Trends from Patent Text
|
|
|
|
In: http://ir.kaist.ac.kr/papers/2008/20081025_2009_SAC_Camera-ready.pdf
|
|
BASE
|
|
Show details
|
|
18 |
Simple Query Translation Methods for Korean-English and Korean-Chinese CLIR in NTCIR Experiments
|
|
|
|
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings3/NTCIR3-CLIR-JangM.pdf
|
|
BASE
|
|
Show details
|
|
19 |
Concept Unification of Terms in Different Languages for IR
|
|
|
|
In: http://acl.ldc.upenn.edu/P/P06/P06-1081.pdf
|
|
BASE
|
|
Show details
|
|
20 |
Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting
|
|
|
|
BASE
|
|
Show details
|
|
|
|