Trend detection from large text data

Abe, Hidenao; Tsumoto, Shusaku

doi:10.1109/icsmc.2010.5641682

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…In order to analyze the relationships between usages of words and phrases in temporally published documents and the difference on a taxonomy, this trend detection method use a temporal pattern extraction method based on data-driven indices [2]. By using the similar terms identified on the basis of temporal patterns of the indices, the method measures their similarities between each term on the taxonomy that can be assumed as the sets of tree structures of concepts on a particular domain.…”

Section: The Methods For Analyzing Distances On Taxonomy and Temporal mentioning

confidence: 99%

“…In order to discover various trends related to usages of the terms in temporally published corpus, the framework [2] is developed as a method for obtaining temporal patterns of an importance index. This framework obtains some temporal patterns based on the importance index from the given temporally published sets of documents.…”

Section: Obtaining Temporal Patterns Of Data-driven Indices Related Tmentioning

confidence: 99%

“…Let us assume that the user wants to explore the evolution of ideas about drugs used to treat migraine 2 By assuming the abstracts and the titles as each corpus, the automatic term extraction is applied. From all of the retrieved abstracts, the automatic term extraction method identifies 61,936 terms.…”

Section: Analyzing Temporal Trends Of Terms and The Similarities On Mmentioning

confidence: 99%

“…Under the above-mentioned motivation, I developed a method for analyzing the similarity of terms on the structured taxonomy and the trend of a data-driven index of the terms [2]. In this chapter, I describe a result of the analysis by using the method for identifying similar terms based on the temporal behavior of usages of each term.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis for Finding Innovative Concepts Based on Temporal Patterns of Terms in Documents

Abe¹

2012

Theory and Applications for Advanced Text Mining

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Section: The Methods For Analyzing Distances On Taxonomy and Temporal mentioning

confidence: 99%

Section: Obtaining Temporal Patterns Of Data-driven Indices Related Tmentioning

confidence: 99%

Section: Analyzing Temporal Trends Of Terms and The Similarities On Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis for Finding Innovative Concepts Based on Temporal Patterns of Terms in Documents

Abe¹

2012

Theory and Applications for Advanced Text Mining

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“…In this paper, we describe a result of the analysis by using the method for identifying similar terms based on the temporal behavior of usages of each term [1]. The temporal pattern extraction method on the basis of term usage index consists of automatic term extraction methods, term importance indices, and temporal clustering in the next section.…”

Section: Introductionmentioning

confidence: 99%

Comparing similarity of concepts identified by temporal patterns of terms in biomedical research documents

Tsumoto¹,

Abe²

2012

2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing

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In this paper, we present an analysis of a rela tionship between temporal trends of automatically extracted terms in medical research document and their similarities on a structured vocabulary. In order to obtain the temporal trends, we used our temporal pattern extraction method that combines an automatic term extraction, an importance index of the terms, and clustering for the values in each period. By using a set of medical research documents that were published every year, we extracted temporal patterns of the automatically extracted terms. Then, we calculated their similarities on the medical taxonomy by defining a distance on the tree structure.For analyzing the relationship between the terms included in the patterns and the similarity of the terms on the taxonomy, the differences of the averaged similarities of the terms in each pattern are compared between the two trends of the temporal patterns.

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Comparing a Clustering Density Criteria of Temporal Patterns of Terms Obtained by Different Feature Sets

Abe

Tsumoto

2011

Rough Sets and Knowledge Technology

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Trend detection from large text data

Cited by 11 publications

References 4 publications

Analysis for Finding Innovative Concepts Based on Temporal Patterns of Terms in Documents

Analysis for Finding Innovative Concepts Based on Temporal Patterns of Terms in Documents

Comparing similarity of concepts identified by temporal patterns of terms in biomedical research documents

Comparing a Clustering Density Criteria of Temporal Patterns of Terms Obtained by Different Feature Sets

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