Overview and semantic issues of text mining

Stavrianou, Anna; Andritsos, Periklis; Nicoloyannis, Nicolas

doi:10.1145/1324185.1324190

Cited by 123 publications

(83 citation statements)

References 45 publications

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“…Concepts (sometimes also referred as compound terms or phrases) are important features used in Text Mining [23]. Compound terms processing is a technique aiming at improving accuracy of search engines by indexing documents according to compound terms, i.e.…”

Section: Related Workmentioning

confidence: 99%

Concepts extraction from unstructured Polish texts: a rule based approach

Szwed

2015

Annals of Computer Science and Information Systems

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Abstract-We present recently developed solution allowing extraction of concepts from unstructured Polish texts with special focus on correct morphological forms of obtained concept names. As Polish is a highly inflected language, detected names need to be transformed following Polish grammar rules. We propose a user-friendly method for specification of transformation patterns, which is based on a simple annotations language. Annotations prepared by a user are compiled into transformation rules. During the concept extraction process the input document is split into sentences and the rules are applied to sequences of words comprised in sentences. Recognized strings forming concept names are aggregated at various levels and assigned with scores. We report also results of initial experiments performed on a medical text.

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Section: Related Workmentioning

confidence: 99%

Concepts extraction from unstructured Polish texts: a rule based approach

Szwed

2015

Annals of Computer Science and Information Systems

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“…텍스트 마이닝은 이러한 다량의 텍스트에 대한 분석을 통해 의미 있는 정보를 추출하는 과 정으로 정의될 수 있다 (Hearst, 1999;Sebastiani, 2002 (Mooney and Bunescu, 2006;Stanvrianou et al, 2007).…”

Section: 텍스트 마이닝unclassified

A Multi-Dimensional Issue Clustering from the Perspective Consumers' Interests and R&D

Hyun¹,

Kim²,

Cho³

2015

Journal of the Korea society of IT services

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The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

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“…According to Stavrianou et al (2007), additional issues that affect text similarity performance and must be considered during text preprocessing are: stopwords and noisy data (e.g. misspelled words) removal, stemming, part of speech (POS) tagging, multi-word terms (collocations), tokenization and text representation.…”

Section: Introductionmentioning

confidence: 99%

A Graph-based Text Similarity Measure That Employs Named Entity Information

Tsekouras¹,

Varlamis

Giannakopoulos

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities' information extracted from the texts and the ngram graphs' model for representing documents. Using OpenCalais as a namedentity recognition service and the JIN-SECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.

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Overview and semantic issues of text mining

Cited by 123 publications

References 45 publications

Concepts extraction from unstructured Polish texts: a rule based approach

Concepts extraction from unstructured Polish texts: a rule based approach

A Multi-Dimensional Issue Clustering from the Perspective Consumers' Interests and R&D

A Graph-based Text Similarity Measure That Employs Named Entity Information

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