Domain specific query generation from natural language text

2016 ◽  
Author(s):  
Anum Iftikhar ◽  
Erum Iftikhar ◽  
Muhammad Khalid Mehmood
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Domain Specific ◽  
Query Generation ◽  
Language Text

scholarly journals Social Marketplace Monitoring and Sentiment Analysis

2019 ◽  
pp. 127-133
Author(s):  
P. Monisha ◽  
R. Rubanya ◽  
N. Malarvizhi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Natural Language Text ◽  
Domain Specific ◽  
Keyboard Input ◽  
On Line ◽  
The Given ◽  
Domain Independent ◽  
Language Text ◽  
Candidate Feature

The overwhelming majority of existing approaches to opinion feature extraction trust mining patterns for one review corpus, ignoring the nontrivial disparities in word spacing characteristics of opinion options across completely different corpora. During this research a unique technique to spot opinion options from on-line reviews by exploiting the distinction in opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrasting corpus). The tendency to capture this inequality called domain relevance (DR), characterizes the relevancy of a term to a text assortment. The tendency to extract an inventory of candidate opinion options from the domain review corpus by shaping a group of grammar dependence rules. for every extracted candidate feature, to have a tendency to estimate its intrinsic-domain relevancy (IDR) and extrinsic-domain relevance(EDR) scores on the domain-dependent and domain-independent corpora, severally. Natural language processing (NLP) refers to computer systems that analyze, attempt understand, or produce one or more human languages, such as English, Japanese, Italian, or Russian. Process information contained in natural language text. The input might be text, spoken language, or keyboard input. The field of NLP is primarily concerned with getting computers to perform useful and interesting tasks with human languages. The field of NLP is secondarily concerned with helping us come to a better understanding of human language

Morality Classification in Natural Language Text

2020 ◽  
pp. 1-1
Author(s):  
Matheus C. Pavan ◽  
Vitor G. Santos ◽  
Alex G. J. Lan ◽  
Joao Martins ◽  
Wesley Ramos Santos ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

scholarly journals Word-based self-indexes for natural language text

2012 ◽  
Vol 30 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Antonio Fariña ◽  
Nieves R. Brisaboa ◽  
Gonzalo Navarro ◽  
Francisco Claude ◽  
Ángeles S. Places ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

CDM: AN APPROACH TO LEARNING IN TEXT CATEGORIZATION

1996 ◽  
Vol 05 (01n02) ◽  
pp. 229-253 ◽  
Author(s):  
JEFFREY L. GOLDBERG
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Machine Learning Algorithms ◽  
Skewed Distribution ◽  
Approach To Learning ◽  
Natural Language Text ◽  
Domain Specific ◽  
Category Discrimination ◽  
Language Text ◽  
Discrimination Method

The Category Discrimination Method (CDM) is a new machine learning algo rithm designed specifically for text categorization. The motivation is there are sta tistical problems associated with natural language text when it is applied as input to existing machine learning algorithms (too much noise, too many features, skewed distribution). The bases of the CDM are research results about the way that humans learn categories and concepts vis-à-vis contrasting concepts. The essential formula is cue validity borrowed from cognitive psychology, and used to select from all possible single word-based features the best predictors of a, given category. The, hypothesis that CDM’s performance. will exceed two non-domain specific al gorithms, Bayesian classification and decision tree learners, is empirically tested.

Wordform as the main basis for analysis of natural language text

2021 ◽  
pp. 101-108
Author(s):  
S.G. Antonov
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Probability Approach ◽  
Correction Problem ◽  
Main Basis ◽  
Language Corpus ◽  
Language Text

In the article discuss the application aspects of wordforms of natural language text for decision the mistakes correction problem. Discuss the merits and demerits of two known approaches for decision – deterministic and based on probabilities/ Construction principles of natural language corpus described, wich apply in probability approach. Declare conclusion about necessity of complex using these approaches in dependence on properties of texts.

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