• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.150-160
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• 2011

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

• Journal of the Korean Society for Library and Information Science
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• v.9
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• pp.99-118
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• 1982

This paper first reviews various techniques proposed for automatic indexing with special emphasis placed on statistical techniques. Frequency-based statistical techniques are categorized into the following three approaches for further investigation on the basis of index term selection criteria: term frequency approach, document frequency approach, and probabilistic approach. In the experimental part of this study, Pao's technique based on the Goffman's transition region formula and Harter's 2-Poisson distribution model with a measure of the potential effectiveness of index term were tested. Experimental document collection consists of 30 agriculture-related documents written in Korean. Pao's technique did not yield good result presumably due to the difference in word usage between Korean and English. However, Harter's model holds some promise for Korean document indexing because the evaluation result from this experiment was similar to that of the Harter's.

• Journal of the Korean Society for information Management
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• v.25 no.2
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• pp.199-221
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• 2008

The purpose of this study is to investigate the characteristics of indexes by human and machine, and differences between them in terms of term identification in a fulltext environment. A back-of-book index and two indexes produced by two term identifiers (LinkIt and Termer) as pseudo-indexing systems for a whole body of a monograph are examined. In the investigation, the traditional contrast between manual and automatic indexing is confirmed in fulltext environment, manual index is for browsing and human use, and automatic index is for searching and machine use. The border between them, however, becomes vague. Some considerations for the use of the term identifiers for browsing and for searching are discussed, and further research for the use of the term identifier is suggested.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.238-240
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• 2002

If there is an Information Retrieval system which comprehends the semantic content of documents and knows the preference of users. the system can search the information better on the Internet, or improve the IR performance. Therefore we propose the IR model which combines semantic based indexing and fuzzy relevance model. In addition to the statistical approach, we chose the semantic approach in indexing, lexical chains, because we assume it would improve the performance of the index term extraction. Furthermore, we combined the semantic based indexing with the fuzzy model, which finds out the exact relevance of the user preference and index terms. The proposed system works as follows: First, the presented system indexes documents by the efficient index term extraction method using lexical chains. And then, if a user tends to retrieve the information from the indexed document collection, the extended IR model calculates and ranks the relevance of user query. user preference and index terms by some metrics. When we experimented each module, semantic based indexing and extended fuzzy model. it gave noticeable results. The combination of these modules is expected to improve the information retrieval performance.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.213-242
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• 1993

During the past decades,several automatic indexing systems have been developed such as single term indexing.phrase indexing and thesaurus basedidndexing systems.Among these systems,single term indexing has been known as superior to others despte its simpicity of extracting meaningful terms.On the other hand,thesaurus based one has been conceived as producing low retrival rate ,mainly because thesauri do not usually have enough index terms.so that much of text data fail to be indexed if they do not match with any of index terms in thesauri.This paper develops a thesaurus based indexing system THINS that yields higher retrieval rate than other systems.by doing syntactic analysis of text data and matching them with index terms in thesauri partially.First,the system analyzes the input text syntactically by using the machine translation suystem MATES/EK and extracts noun phrases.After deleting stop words from noun phrases and stemming the remaining ones.it tries to index these with similar index terms in the thesaurus as much as possible. We conduct an experiment with CACM data set that measures the retrieval effectiveness with CACM data set that measures the retrieval effectuvenss of THINS with single term based one under HYKIS-a thesaurus based information retrieval system.It turns out that THINS yields about 10 percent higher precision than single term based one.while shows 8to9 percent lower recall.This retrieval rate shows that THINS improves much better than privious ones that only yields 25 or 30 percent lower precision than single term based one.We also argue that the relatively lower recall is cause by that CRCS-the thesaurus included in CACM datea set is very incomplete one,having only more than one thousand terms,thus THINS is expected to produce much higher rate if it is associated with currently available large thesaurus.

• MALSORI
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• no.67
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• pp.149-166
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• 2008

In spoken document retrievals (SDR), subword (typically phonemes) indexing term is used to avoid the out-of-vocabulary (OOV) problem. It makes the indexing and retrieval process independent from any vocabulary. It also requires a small corpus to train the acoustic model. However, subword indexing term approach has a major drawback. It shows higher word error rates than the large vocabulary continuous speech recognition (LVCSR) system. In this paper, we propose an probabilistic slot detection and n-gram based string matching method for phone based spoken document retrievals to overcome high error rates of phone recognizer. Experimental results have shown 9.25% relative improvement in the mean average precision (mAP) with 1.7 times speed up in comparison with the baseline system.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.451-462
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• 2009

The aim of this study is to apply latent semantic indexing(LSI) techniques for efficient automatic classification of opinionated documents. For the experiments, we collected 1,000 opinionated documents such as reviews and news, with 500 among them labelled as positive documents and the remaining 500 as negative. In this study, sets of content words and sentiment words were extracted using a POS tagger in order to identify the optimal feature set in opinion classification. Findings addressed that it was more effective to employ LSI techniques than using a term indexing method in sentiment classification. The best performance was achieved by a supervised LSI technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2211-2232
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• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.

• Journal of the Korean Society for information Management
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• v.5 no.2
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• pp.99-126
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• 1988

The purpose of this ptudy is to presenet a relevant automaitc technigue in accordance with the statistical term characteristie in a collection comprising different subjecits, by comparing and evaluating two automatic indexing technigues (Inverse Document Fregnency Weighting Technigue and Term Discrimiantion Value Weighting Technigues) intht fields of Pharmacology and Library & Information Science.

• Journal of the Korean Society for information Management
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• v.4 no.1
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• pp.47-86
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• 1987

The purpose of this study is to present an effective automatic indexing method of Korean texts based on statistical criteria. Titles and abstracts of the 299 documents randomly selected from ETRI's DOCUMENT data base are used as the experimental data in this study the experimental data is divided into 4 word groups and these 4 word groups are respectively analyzed and evaluated by applying 3 automatic indexing methods including Transition Phenomena of Word Occurrence, Inverse Document Frequency Weighting Technique, and Term Discrimination Weighting Technique.