Linguistic Rule-Based Ontology-Driven Chatbot System

2018 ◽  
pp. 47-57 ◽  
Author(s):  
Anuj Saini ◽  
Aayushi Verma ◽  
Anuja Arora ◽  
Chetna Gupta
Keyword(s):  
Rule Based ◽  
Linguistic Rule

DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE-BASED APPROACH1

2012 ◽  
Vol 29 (3) ◽  
pp. 390-416 ◽  
Author(s):  
Sophia Yat Mei Lee ◽  
Ying Chen ◽  
Chu-Ren Huang ◽  
Shoushan Li
Keyword(s):  
Rule Based ◽  
Linguistic Rule

Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

Remote sensing and GIS-based landslide risk assessment using a linguistic rule-based fuzzy approach

2006 ◽  
Author(s):  
Debi Prasanna Kanungo ◽  
Manoj Kumar Arora ◽  
Shantanu Sarkar ◽  
Ravi Prakash Gupta
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Remote Sensing And Gis ◽  
Landslide Risk ◽  
Fuzzy Approach ◽  
Rule Based ◽  
Linguistic Rule ◽  
Landslide Risk Assessment

Unsupervised Learning and Linguistic Rule Based Algorithm for Uyghur Word Segmentation

2014 ◽  
Vol 9 (5) ◽  
Author(s):  
Turdi Tohti ◽  
Winira Musajan ◽  
Askar Hamdulla
Keyword(s):  
Unsupervised Learning ◽  
Word Segmentation ◽  
Rule Based ◽  
Linguistic Rule

scholarly journals A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents

2011 ◽  
Vol 12 (S2) ◽  
Author(s):  
Isabel Segura-Bedmar ◽  
Paloma Martínez ◽  
César de Pablo-Sánchez
Keyword(s):  
Drug Interactions ◽  
Rule Based ◽  
Linguistic Rule ◽  
Rule Based Approach

scholarly journals Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 1-19
Author(s):  
Nikhlesh Pathik ◽  
Pragya Shukla
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

Tagging Icelandic text: A linguistic rule-based approach

2008 ◽  
Vol 31 (1) ◽  
pp. 47-72 ◽  
Author(s):  
Hrafn Loftsson
Keyword(s):  
Substantial Improvement ◽  
Data Driven ◽  
Rule Based ◽  
Functional Roles ◽  
Rule Based System ◽  
Linguistic Rule ◽  
Part Of Speech ◽  
Unknown Words ◽  
Main Components ◽  
Rule Based Approach

The Icelandic language is a morphologically complex language, for which a large tagset has been created. This paper describes the design of a linguistic rule-based system for part-of-speech tagging Icelandic text. The system contains two main components: a disambiguator, IceTagger, and an unknown word guesser, IceMorphy. IceTagger uses a small number of local elimination rules along with a global heuristics component. The heuristics guess the functional roles of the words in a sentence, mark prepositional phrases, and use the acquired knowledge to force feature agreement where appropriate. IceMorphy is used for guessing the tag profile for unknown words and for automatically filling tag profile gaps in the lexicon. Evaluation shows that IceTagger achieves 91.54% accuracy, a substantial improvement on the highest accuracy, 90.44%, obtained using three state-of-the-art data-driven taggers. Furthermore, the accuracy increases to 92.95% by using IceTagger along with two data-driven taggers in a simple voting scheme. The development time of the tagging system was only seven man-months, which can be considered a short development period for a linguistic rule-based system.

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