Conceptual ascendant feature extraction of a financial corpus

Jaoua, Ali; Alja’am, Jihad Mohamad; Hammami, Helmi; Ferjani, Fethi; Laban, Firas; Semmar, Nasredine; Essafi, Hassane; Elloumi, Samir

doi:10.1109/pic.2010.5687877

Cited by 1 publication

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have considered two data sets: the FW-Data (Jaoua et al 2010) and the 20-newsgroup data sets (Rennie 2008) (Matlab/Octave version). FW -Data FW-Data is a collection of approximately 400 TDs collected in the scope of the Financial Watch Project (Jaoua et al 2010) with the collaboration of the CEA-LIST institute a . The documents have been already classified in three categories according to the predefined classes in the financial domain which are management change, performance, and transaction.…”

Section: Methodsmentioning

confidence: 99%

“…BOC are closely related to mathematical FCA settings. FCA has been shown to provide a theoretical framework for the efficient resolution of many practical problems from data mining (Rodriguez-Esteban 2019), conceptual reasoning (Elloumi et al 2014), software engineering, and information retrieval (Ganter and Wille 1999; Weiss et al 2005; Jaoua et al 2010). An interesting problem is to find a representation of an FCT by minimal or an optimal set of concepts, particularly, in the case of textual data.…”

Section: Related Workmentioning

confidence: 99%

“…In the case of new collected data sets, for example, the data collected in the scope of the Financial Watch Project (FW-Data)(Jaoua et al 2010), we recommend to apply a pre-processing phase before starting the TFS as indicated in the < optional > step. It consists in applying the natural language processing steps (e.g., stopword removal, synonymy processing) to prepare the FCT construction.…”

Section: Using N-composite Isolated Points For Textual Feature Selectionmentioning

confidence: 99%

See 2 more Smart Citations

A new approach for textual feature selection based on N-composite isolated labels

Elloumi

2019

Nat. Lang. Eng.

Self Cite

View full text Add to dashboard Cite

Textual Feature Selection (TFS) aims to extract relevant parts or segments from text as being the most relevant ones w.r.t. the information it expresses. The selected features are useful for automatic indexing, summarization, document categorization, knowledge discovery, so on. Regarding the huge amount of electronic textual data daily published, many challenges related to the semantic aspect as well as the processing efficiency are addressed. In this paper, we propose a new approach for TFS based on Formal Concept Analysis background. Mainly, we propose to extract textual features by exploring the regularities in a formal context where isolated points exist. We introduce the notion of N-composite isolated points as a set of N words to be considered as a unique textual feature. We show that a reduced value of N (between 1 and 3) allows extracting significant textual features compared with existing approaches even for non-completely covering an initial formal context.

show abstract