• Journal of Information Science Theory and Practice
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• v.1 no.3
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• pp.33-46
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• 2013

Despite limited success, today's information retrieval (IR) systems are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguation approaches, which rely on textual context, are ineffective against weak queries that are typically short. Motivated by the demand for a robust IR system that can consistently provide highly accurate results, the proposed study implemented a novel topic detection that leveraged both the language model and structural knowledge of Wikipedia and systematically evaluated the effect of query disambiguation and topic-based retrieval approaches on TREC collections. The results not only confirm the effectiveness of the proposed topic detection and topic-based retrieval approaches but also demonstrate that query disambiguation does not improve IR as expected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2468-2487
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• 2015

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.79-88
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• 2015

The web search engines are exposed to the issue of uncertainty because of ambiguous queries, being input for retrieving the accurate results. Ambiguous queries constitute a significant fraction of such instances and pose real challenges to web search engines. Moreover, web search has created an interest for the researchers to deal with search by considering context in terms of location perspective. Our proposed disambiguation approach is designed to improve user experience by using context in terms of location relevance with the document relevance. The aim is that providing the user a comprehensive location perspective of a topic is informative than retrieving a result that only contains temporal or context information. The capacity to use this information in a location manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or clustering based on location. In order to carry out the approach, we developed a Java based prototype to derive the contextual information from the web results based on the queries from the well-known datasets. Among those results, queries are further classified in order to perform search in a broad way. After the result provision to users and the selection made by them, feedback is recorded implicitly to improve the web search based on contextual information. The experiment results demonstrate the outstanding performance of our approach in terms of precision 75%, accuracy 73%; recall 81% and f-measure 78% when compared with generic temporal evaluation approach and furthermore achieved precision 86%, accuracy 71%; recall 67% and f-measure 75% when compared with web document clustering approach.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• Journal of the Korean Society for information Management
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• v.26 no.4
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• pp.81-92
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• 2009

This paper proposes a technique for improving performance using word senses and user feedback in web information retrieval, compared with the retrieval based on ambiguous user query and index. Disambiguation using query word senses can eliminating the irrelevant pages from the search result. According to semantic categories of nouns which are used as index for retrieval, we build the word sense knowledge-base and categorize the web pages. It can improve the precision of retrieval system with user feedback deciding the query sense and information seeking behavior to pages.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.777-782
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• 2012

This paper proposes a technique for improving performance using word senses and user feedback in web information retrieval, compared with the retrieval based on ambiguous user query and index. Disambiguation using query word senses can eliminating the irrelevant pages from the search result. According to semantic categories of nouns which are used as index for retrieval, we build the word sense knowledge-base and categorize the web pages. It can improve the precision of retrieval system with user feedback deciding the query sense and information seeking behavior to pages.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.280-285
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• 2007

This paper proposes a technique improving performance using word senses and user feedback in web information retrieval, compared with the retrieval based on ambiguous user query and index. Disambiguation using word senses is very important processing for improving performance by eliminating the irrelevant pages from the result. According to semantic categories of nouns which are used as index for retrieval, we build the word sense knowledge-base and categorize the web pages. It can improve the performance of retrieval system with user feedback deciding the query sense and information seeking behavior to web pages.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1073-1089
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• 2006

The goal of Natural Language Processing(NLP) is to make a computer understand a natural language and to deliver the meanings of natural language to humans. Word sense Disambiguation(WSD is a very important technology to achieve the goal of NLP. In this paper, we describe a technology for automatic homonyms disambiguation using both Mutual Information(MI) and a Sense-Tagged Compound Noun Dictionary. Previous research work using word definitions in dictionary suffered from the problem of data sparseness because of the use of exact word matching. Our work overcomes this problem by using MI which is an association measure between words. To reflect language features, the rate of word-pairs with MI values, sense frequency and site of word definitions are used as weights in our system. We constructed a Sense-Tagged Compound Noun Dictionary for high frequency compound nouns and used it to resolve homonym sense disambiguation. Experimental data for testing and evaluating our system is constructed from QA(Question Answering) test data which consisted of about 200 query sentences and answer paragraphs. We performed 4 types of experiments. In case of being used only MI, the result of experiment showed a precision of 65.06%. When we used the weighted values, we achieved a precision of 85.35% and when we used the Sense-Tagged Compound Noun Dictionary, we achieved a precision of 88.82%, respectively.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.547-550
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• 2006

The criterion which judges an information retrieval system performance is to how many accurately retrieve an information that the user wants. The search result which uses only homograph has been appears the various documents that relates to each meaning of the word or intensively appears the documents that relates to specific meaning of it. So in this paper, we suggest semantic information retrieval technique using relation within User-Word Intelligent Network(U-WIN) to solve a disambiguation of query In our experiment, queries divide into two classes, the homograph used in terminology and the general homograph, and it sets the expansion query forms at "query + hypemym". Thus we found that only web document search's precision is average 73.5% and integrated search's precision is average 70% in two portal site. It means that U-WIN-Based semantic information retrieval technique can be used efficiently for a IR system.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.52-58
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• 1997

정보 검색에서는 질의문과 문서를 동일한 표현으로 변환시켜 관련성을 비교하게 된다. 특히 질의문과 문서의 언어가 서로 다른 교차 언어 문서 검색 (CLTR : Cross-Language Text Retrieval) 에서 이러한 변환 과정은 언어 변환을 수반하게 된다. 교차 언어 문서 검색의 기존 연구에는 사전, 말뭉치, 기계 번역 등을 이용한 방법들이 있다. 일반적으로 언어간 변환에는 필연적으로 의미의 중의성이 발생되며 사전에 기반한 기존 연구에서는 다의어의 중의성 의미해소를 고려치 않고 있다. 본 연구에서는 질의어의 언어 변환시 한-일 대역어 사전 및 카도가와 시소러스 (각천(角川) 시소러스) 에 기반한 질의어 중의성 해소 방법과 공기하는 대역어를 갖는 문서에 가중치를 부여하는 방법을 제안한다. 제안된 방법들은 일본어 특허 문서를 대상으로 실험하였으며 5 %의 정확도 향상을 얻을 수 있었다.