Applying data mining techniques for descriptive phrase extraction in digital document collections

Ahonen, Helena; Op, Heinonen; Klemettinen, Mika; Verkamo, A. Inkeri

doi:10.1109/adl.1998.670374

Cited by 51 publications

(33 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In phrase based method document is analyzed on phrase basis as phrases are less ambiguous and more discriminative than individual terms [2]. The likely reasons for the daunting performance include: 1) Phrases have inferior statistical properties to terms, 2) They have low frequency of occurrence, and 3) Large numbers of redundant and noisy phrases are present among them.…”

Section: Phrase Based Methodsmentioning

confidence: 99%

Text Mining Methods and Techniques

Gaikwad¹,

Chaugule²,

Patil³

2014

IJCA

106

View full text Add to dashboard Cite

In recent years growth of digital data is increasing, knowledge discovery and data mining have attracted great attention with coming up need for turning such data into useful information and knowledge. The use of the information and knowledge extracted from a large amount of data benefits many applications like market analysis and business management. In many applications database stores information in text form so text mining is the one of the most resent area for research. To extract user required information is the challenging issue. Text Mining is an important step of knowledge discovery process. Text mining extracts hidden information from notstructured to semi-structured data. Text mining is the discovery by automatically extracting information from different written resources and also by computer for extracting new, previously unknown information. This survey paper tries to cover the text mining techniques and methods that solve these challenges. In this survey paper we discuss such successful techniques and methods to give effectiveness over information retrieval in text mining. The types of situations where each technology may be useful in order to help users are also discussed.

show abstract

Section: Phrase Based Methodsmentioning

confidence: 99%

Text Mining Methods and Techniques

Gaikwad¹,

Chaugule²,

Patil³

2014

IJCA

106

View full text Add to dashboard Cite

show abstract

“…Our research mainly focuses on the statistical approach, which does not need any grammatical knowledge and has easy adaptability to other languages. Statistical phrase-finding approaches have been used for expanding vector dimensions in clustering multiple documents [21,22], or finding more descriptive or important/meaningful phrases [1,2]. This paper compares previous statistical approaches and attempts to find meaningful phrases in a document.…”

Section: Introductionmentioning

confidence: 99%

“…Ahonen et al [1], Zamir and Etzioni [23], and Chan [2] introduced phrase-finding algorithms. Ahonen's algorithm depends on conditional probability and needs a fixed maximum phrase length.…”

Section: Introductionmentioning

confidence: 99%

Identifying variable-length meaningful phrases with correlation functions

Kim

Chan

16th IEEE International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…A sequence S = ¢s 1 , s 2 ,…, s n ² (s i Ӈ T) is an ordered list of terms. A sequence Į = ¢a 1 , a 2 ,…, a n ² is a sub-sequence of another sequence ȕ = ¢b 1 , b 2 …”

Section: Basic Definitionmentioning

confidence: 99%

“…The version of this data collection we chose is Reuters Corpus Volume 1 (RCV1) 2 , which includes 806,791 news stories. The English language stories are produced by Rueters journalists for the period between 20 August 1996 and 19 August 1997.…”

Section: Real World Datasetsmentioning

confidence: 99%