Language Structure Using Fuzzy Similarity

Chaudhari, Narendra S.; Xiang-rui, Wang

doi:10.1109/tfuzz.2009.2020155

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Alignment based learning (ABL) [10,11,19,20] is based on alignment information. In ABL Pairwise alignment for each pair of the input sentences is done by finding equal parts and unequal parts.…”

Section: Discriminative Context-free Grammarmentioning

confidence: 99%

Segmentation of Document Using Discriminative Contextfree Grammar Inference and Alignment Similarities

Thakur¹

2015

IJRITCC

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Abstract-Text Documents present a great challenge to the field of document recognition. Automatic segmentation and layout analysis of documents is used for interpretation and machine translation of documents. Document such as research papers, address book, news etc. is available in the form of un-structured format. Extracting relevant Knowledge from this document has been recognized as promising task. Extracting interesting rules form it is complex and tedious process. Conditional random fields (CRFs) utilizing contextual information, hand-coded wrappers to label the text (such as Name, Phone number and Address etc). In this paper we propose a novel approach to infer grammar rules using alignment similarity and discriminative context-free grammar. It helps in extracting desired information from the document.

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Section: Discriminative Context-free Grammarmentioning

confidence: 99%

Segmentation of Document Using Discriminative Contextfree Grammar Inference and Alignment Similarities

Thakur¹

2015

IJRITCC

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“…Alignment based Learning (ABL) [1] [12]- [14] is based on alignment information. In ABL pairwise alignment for each pair of the input sentences is done by finding equal parts and unequal parts.…”

Section: Alignment Profile For Label Selectionmentioning

confidence: 99%

Information extraction from semi-structured and un-structured documents using probabilistic context free grammar inference

Thakur¹,

Jain

Chaudhari

et al. 2012

2012 International Conference on Information Retrieval &Amp; Knowledge Management

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Large number of research papers are available in the form of un-structured (text) format. Knowledge discovery in un-structured document has been recognized as promising task. These documents are typically formatted for human viewing, which varies widely from document to document. Frequent change in their formatting causes difficulties in constructing a global schema. Thus, discovery of interesting rules from it is a complex and tedious process. Recently, conditional random fields (CRFs) and hand-coded wrappers have been used to label the text (such as Title, Author Name(s), Affiliation, Email, Contact number, etc. in research papers). In this paper we propose a novel hybrid approach to infer grammar rules using alignment similarity and probabilistic context free grammar. It helps in extracting desired information from the document.

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“…Prefix tree acceptors are often constructed from the given sample as a starting DFA, and they are useful for modeling positive samples. Other approaches include learning by queries [6], learning by structural information [7], learning subclass of language [8], learning by genetic algorithm [9], neural networks [10], Markov approaches [11] and other related work can be found in [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

User Behavior Analysis Using Alignment Based Grammatical Inference from Web Server Access Log

Thakur¹,

Jain²,

Chaudhari³

2013

IJFCC

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Abstract-Application of data mining technique to the WorldWide Web refers to as Web mining. Web based origination collects large volume of data for their operation. Analysis of such data can help the organization for better working (Marketing strategy, services, evaluation of effectiveness, promotional campaigns etc). This type of analysis require discovery of meaningful relationships from the large collection of primarily unstructured data stored in Web server access logs. We propose a new approach for automatically learning (context-free) grammar rules form server access log text (positive set) samples, based on the alignments between the sentences. Our approach works on pairs of unstructured sentences that have one or more words common.Index Terms-Web usage mining, computational learning, grammatical inference, alignment profile, information extraction.

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Language Structure Using Fuzzy Similarity

Cited by 7 publications

References 32 publications

Segmentation of Document Using Discriminative Contextfree Grammar Inference and Alignment Similarities

Segmentation of Document Using Discriminative Contextfree Grammar Inference and Alignment Similarities

Information extraction from semi-structured and un-structured documents using probabilistic context free grammar inference

User Behavior Analysis Using Alignment Based Grammatical Inference from Web Server Access Log

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