A testbed for Indonesian text retrieval (original) (raw)
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1997
Abstract We evaluate the effectiveness of a dictionary-based cross-language text retrieval technique which uses a two-way dictionary for translating queries from their original language into the language of the text documents. As can be expected, the translated queries are not as effective as queries formulated by the users using the same language as the text documents. We then apply a local-feedback technique to expand the translated queries in order to improve their retrieval effectiveness.
Word Stemming Algorithms and Retrieval Effectiveness in Malay and Arabic Documents Retrieval Systems
2007
Documents retrieval in Information Retrieval Systems (IRS) is generally about understanding of information in the documents concern. The more the system able to understand the contents of documents the more effective will be the retrieval outcomes. But understanding of the contents is a very complex task. Conventional IRS apply algorithms that can only approximate the meaning of document contents through keywords approach using vector space model.
Comparison of VSM, GVSM, and Lsi in Information Retrieval for Indonesian Text
Jurnal Teknologi, 2016
Vector space model (VSM) is an Information Retrieval (IR) system model that represents query and documents as n-dimension vector. GVSM is an expansion from VSM that represents the documents base on similarity value between query and minterm vector space of documents collection. Minterm vector is defined by the term in query. Therefore, in retrieving a document can be done base on word meaning inside the query. On the contrary, a document can consist the same information semantically. LSI is a method implemented in IR system to retrieve document base on overall meaning of users’ query input from a document, not based on each word translation. LSI uses a matrix algebra technique namely Singular Value Decomposition (SVD). This study discusses the performance of VSM, GVSM and LSI that are implemented on IR to retrieve Indonesian sentences document of .pdf, .doc and .docx extension type files, by using Nazief and Adriani stemming algorithm. Each method implemented either by thread or no-...
Implementation Of Information Retrieval Indonesian Text Documents Using The Vector Space Model
Information search (usually a document) that is based on a query (user input) which is expected to meet user wishes of a collection of documents known as Information Retrieval. This study discusses the implementation of information retrieval to find and match the Indonesian language text documents using the Vector Space Retrieval Model. goal is to provide a solution in search engines to provide information matching text in the database using specific keywords, the result of matching is presented in the form of ratings.
2004
Our information retrieval system takes advantage of numerous characteristics of information and uses numerous sophisticated techniques. It uses Robertson's 2-Poisson model and Rocchio's formula, both of which are known to be effective. Characteristics of newspapers such as locational information are used. We present our application of Fujita's method, where longer terms are used in retrieval by the system but de-emphasized relative to the emphasis on the shortest terms. This allows us to use both compound and single-word terms. The statistical test used in expanding queries through an automatic feedback process is described. The method gives us terms that have been statistically shown to be related to the top-ranked documents obtained in the first retrieval. We also use a numerical term, QIDF, which is an IDF term for queries. QIDF decreases the scores for stop words that occur in many queries. It can be very useful for foreign languages for which we cannot determine stop words. We also use web-based unknown word translation for bilingual information retrieval. We participated in two monolingual information retrieval tasks (Korean and Japanese) and five bilingual information retrieval tasks (Chinese-Japanese, English-Japanese, Japanese-Korean, Korean-Japanese, and English-Korean) at NTCIR-6. We obtained good results in all the tasks.
Applying Multiple Characteristics and Techniques in the NICT Information Retrieval System at NTCIR-6
2004
Our information retrieval system takes advantage of numerous characteristics of information and uses numerous sophisticated techniques. It uses Robertson's 2-Poisson model and Rocchio's formula, both of which are known to be effective. Characteristics of newspapers such as locational information are used. We present our application of Fujita's method, where longer terms are used in retrieval by the system but de-emphasized relative to the emphasis on the shortest terms. This allows us to use both compound and single-word terms. The statistical test used in expanding queries through an automatic feedback process is described. The method gives us terms that have been statistically shown to be related to the top-ranked documents obtained in the first retrieval. We also use a numerical term, QIDF, which is an IDF term for queries. QIDF decreases the scores for stop words that occur in many queries. It can be very useful for foreign languages for which we cannot determine stop words. We also use web-based unknown word translation for bilingual information retrieval. We participated in two monolingual information retrieval tasks (Korean and Japanese) and five bilingual information retrieval tasks (Chinese-Japanese, English-Japanese, Japanese-Korean, Korean-Japanese, and English-Korean) at NTCIR-6. We obtained good results in all the tasks.
Evaluation of Information Retrieval Systems: Test Collections and Evaluation Workshops
The crucial role of the evaluation in the development of the information retrieval tools is useful evidence to improve the performance of these tools and the quality of results that they return. However, the classic evaluation approaches have limitations and shortcomings especially regarding to the user consideration, the measure of the adequacy between the query and the returned documents and the consideration of characteristics, specifications and behaviors of the search tool. Therefore, we believe that the exploitation of contextual elements could be a very good way to evaluate the search tools. So, this paper presents a new approach that takes into account the context during the evaluation process at three complementary levels. The experiments gives at the end of this article has shown the applicability of the proposed approach to real research tools.
Towards Universal Text Retrieval: Tipster Text Retrieval Research at New Mexico State University
Information Retrieval
New Mexico State University's Computing Research Lab has participated in research in all three phases of the US Government's Tipster program. Our work on information retrieval has focused on research and development of multilingual and cross-language approaches to automatic retrieval. The work on automatic systems has been supplemented by additional research into the role of the IR system user in interactive retrieval scenarios: monolingual, multilingual and cross-language. The combined efforts suggest that “universal” text retrieval, in which a user can find, access and use documents in the face of language differences and information overload, may be possible.