WordNet Based Information Retrieval System for Assamese

Barman, Anup Kumar; Sarmah, Jumi; Sarma, Shikhar Kumar

doi:10.1109/uksim.2013.90

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…One paper improved the search results by using the Vector Space Model and Assamese WordNet. [14] Another paper developed a logical method to answer questions in Assamese. [15] This paper contributed to AI and NLP for the Assamese language and other languages and cultures.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of an Assamese Language Chatbot Using Neural Networks

Sarma,

Pathak

2023

IJSRCSE

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This research paper proposes a design for an AI chatbot that can understand Assamese language using a feedforward neural network. We have developed an Assamese language dataset and trained the model on it. We have used the bag of words method for feature extraction of the text. We have evaluated the accuracy and performance of the model using metrics like precision, recall and F1 score. The model has achieved an accuracy of 89.11%, which is higher than SVM and Naive Bayes models. We have also presented the future plan of this research. The novelty of this research is that we have developed a deep learning FFNN model for an AI Assamese chatbot along with an Assamese dataset, which has not been done before. The significance of this research is that it will help to explore the AI Assamese chatbot domain and inspire new ideas for researchers, as well as help the Assamese society to use their language in digital platforms.

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Section: Related Workmentioning

confidence: 99%

Design and Implementation of an Assamese Language Chatbot Using Neural Networks

Sarma,

Pathak

2023

IJSRCSE

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“…Many of the works have concentrated on the use of WordNet to improve the IR performance. Many studies extended the query using all synonyms contained in a synset which contains query terms [11,41,42].The rest of other approaches set all synonyms of the synset, which contain query terms as CET. They then use the word sense disambiguate (WSD) approach to determine the right sense synsets.…”

Section: B Automatic Query Expansion Approachesmentioning

confidence: 99%

“…The query is expanded by subjoining either statistically related terms to the terms of the original query or semantically related terms chosen from some lexical database. Some statistical QE approaches in [6,7,8,9] and semantic QE approaches in [10,11,12,13,14,15,16,17] expand a query outperform IR model that ignores the proximity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Alnofaie¹,

Dahab²,

Kamal³

2016

ijacsa

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Abstract-Most of the information retrieval (IR) models rank the documents by computing a score using only the lexicographical query terms or frequency information of the query terms in the document. These models have a limitation as they does not consider the terms proximity in the document or the term-mismatch or both of the two. The terms proximity information is an important factor that determines the relatedness of the document to the query. The ranking functions of the Spectral-Based Information Retrieval Model (SBIRM) consider the query terms frequency and proximity in the document by comparing the signals of the query terms in the spectral domain instead of the spatial domain using Discrete Wavelet Transform (DWT). The query expansion (QE) approaches are used to overcome the word-mismatch problem by adding terms to query, which have related meaning with the query. The QE approaches are divided to statistical approach Kullback-Leibler divergence (KLD) and semantic approach P-WNET that uses WordNet. These approaches enhance the performance. Based on the foregoing considerations, the objective of this research is to build an efficient QESBIRM that combines QE and proximity SBIRM by implementing the SBIRM using the DWT and KLD or P-WNET. The experiments conducted to test and evaluate the QESBIRM using Text Retrieval Conference (TREC) dataset. The result shows that the SBIRM with the KLD or P-WNET model outperform the SBIRM model in precision (P@), R-precision, Geometric Mean Average Precision (GMAP) and Mean Average Precision (MAP).

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“…Synset consists of nouns, verbs,adjectives, and adverbs. Many IR systems [10] have been developed which uses WordNet for Query Expansion considering the fact that lack of query term in document does not mean that document is irrelevant. If any synonym of the query exists in document then also document is said to be related to the search query.…”

Section: Related Workmentioning

confidence: 99%

Extended Semantic based Boolean Information Retrieval Algorithm for User-driven Query

Upama¹,

Shah²

2015

IJERT

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Information Retrieval (IR) is essentially a matter of deciding which documents within a large collection satisfies a user's information need. Those documents are called relevant documents and the documents that are not of the topic specified by the user are said to be non-relevant. An existing SBIR algorithm uses lexical database, WordNet to find synonyms of single-word query term considering that the absence of the given term in a document does not necessarily mean that the document is not a relevant.In this paper, a new algorithm is proposed which works with compound terms and uses modified Porter Stemming Algorithm to solve some stemming errors found in Porter Stemmer Algorithm proposed by M. F. Porter. This will improve the recall as more relevant documents will be retrieved. We propose to involve a user in the search process through interactive feedback for word senses. This will further improve recall by retrieving more user relevant results.

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WordNet Based Information Retrieval System for Assamese

Cited by 9 publications

References 6 publications

Design and Implementation of an Assamese Language Chatbot Using Neural Networks

Design and Implementation of an Assamese Language Chatbot Using Neural Networks

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Extended Semantic based Boolean Information Retrieval Algorithm for User-driven Query

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