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Modified lesk algorithm for word sense disambiguation in Bengali

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第2期49卷 1-12页

作者： Das, Ratul Pal, Alok Ranjan Saha, Diganta Jadavpur Univ Dept Comp Sci & Engn Kolkata West Bengal India Coll Engn & Management Dept Comp Sci & Engn Kolaghat West Bengal India

This article presents a novel approach towards solving the problem of Word Sense Disambiguation (WSD) for Bengali Text. The algorithm used in this work is a modification of lesk algorithm. In the original algorithm, the overlap between the "context bag" and the "sense bag" items from the lexical resource (WordNet) are calculated using word pair matching. In the current approach the overlap is calculated by adopting semantic similarity measure using the fastText subword embeddings. The approach can efficiently handle unknown wordforms and discover the latent semantics of words. Significant progress has been made in WSD for English and other European Languages. Indian languages like Bengali still pose a formidable challenge. The dataset used for the work is individual sentences from the Bengali Wikipedia which is a huge collection of Bengali text ( 96 K Webpages with 1700 K sentences), the Indo WordNet for Bengali language and Bengali Online Dictionary. The results of the experiments performed are promising. The target words which have semantically distinct synsets in the WordNet give a high F1 score. The F1 score achieved is 80% which is well over the baseline and shows significant improvement over the other knowledge-based approaches tried on low resource Indian languages.

关键词： Word sense disambiguation lesk algorithm fastText Indo WordNet semantic similarity measure word embeddings

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Integrating lesk algorithm with Cosine Semantic Similarity to Resolve Polysemy for Setswana Language

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 772-778页

作者： Moape, Tebatso Gorgina Olugbara, Oludayo O. Ojo, Sunday O. Durban Univ Technol Dept Informat Technol Durban South Africa

Word Sense Disambiguation (WSD) serves as an intermediate task for enhancing text understanding in Natural Language Processing (NLP) applications, including machine translation, information retrieval, and text summarization. Its role is to enhance the effectiveness and efficiency of these applications by ensuring the accurate selection of the appropriate sense for polysemous words in diverse contexts. This task is recognized as an AI-complete problem, indicating its longstanding complexity since the 1950s. One of the earliest proposed solutions to address polysemy in NLP is the lesk algorithm, which has seen various adaptations by researchers for different languages over the years. This study proposes a simplified, lesk-based algorithm to resolve polysemy for Setswana. Instead of combinatorial comparisons among candidate senses that lesk is based on that cause computational complexity, this study models word sense glosses using Bidirectional Encoder Representations from Transformers (BERT) and Cosine similarity measure, which have been proven to achieve optimal performance in WSD. The proposed algorithm was evaluated on Setswana and obtained an accuracy of 86.66 and an error rate of 14.34, surpassing the accuracy of other lesk-based algorithms for other languages.

关键词： Word sense disambiguation lesk algorithm cosine similarity Bidirectional Encoder Representations from Transformers (BERT)

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Word Sense Disambiguation (WSD) for Indonesian Homograph Word Meaning Determination by lesk algorithm Application 12

Word Sense Disambiguation (WSD) for Indonesian Homograph Wor...

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12th International Conference on Information and Communication Technology and System (ICTS)

作者： Basuki, Setio Kholimi, Ali Sofyan Minarno, Agus Eko Sumadi, Fauzi Dwi Setiawan Effendy, M. Rizal Arif Univ Muhammadiyah Malang Fac Engn Dept Informat Malang Indonesia

ISBN: (纸本)9781728121338

Indonesian has several words which are commonly known as ambiguous words, confusing the meaning of a sentence or a statement to be less understood or even not delivered. It is different to a human perception which has linguistic ability to determine the meaning of ambiguity or more than a word meaning. Word Sense Disambiguation is one of a topic from natural language processing (NLP) which deals with ambiguity handling. Word Sense Disambiguation is a linguistic computational process which aims to identify the proper meaning of words based on the context. This current study is designed as a system to handle the ambiguous words. It is conducted by looking up and defining the meaning of ambiguous words by using lesk algorithm. The test is performed towards the functionality from a system in which the result system test is in line with test data from KBBI. The result presents accuracy level of 78.6% for one of an ambiguous word and 62.5 % for two of ambiguous words in determining the meaning appropriately.

关键词： Word Sense Disambiguation lesk algorithm Homograph Ambiguity

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WordNet Semantic Relations Based Enhancement of KNN Model for Implicit Aspect Identification in Sentiment Analysis

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 1-14页

作者： Benarafa, Halima Benkhalifa, Mohammed Akhloufi, Moulay Mohammed V Univ Rabat Algorithms Networks Intelligent Syst & Software En Dept Comp Sci Fac Sci Rabat Morocco Moncton Univ Percept Robot & Intelligent Machines Res Grp PRIME Dept Comp Sci Moncton NB Canada

Opinion mining or sentiment analysis (SA) is a key component of real-world applications for e-commerce organizations, manufacturers, and customers. SA deals with the computational evaluation of people's views, thoughts, and feelings in the text, whether they are visible or concealed. The Aspect based SA level is becoming one of the most active phases in this area. In this paper, we propose an approach to enrich K-Nearest Neighbors (KNN) to deal with Implicit Aspect Identification task (IAI). Through the use of WordNet semantic relations, we propose an enhancement for KNN distance computation to support the IAI task. For a conclusive empirical evaluation, experiments are conducted on two datasets of electronic products and restaurant reviews and the effects of our approach are examined and analyzed according to three criteria: KNN distance used to compute the similarity, the number of nearest neighbors (K) and the KNN behavior towards Overfitting and Underfitting. The experimental results show that our approach helps KNN improve its performance and better deal with Overfitting and Underfitting for Implicit Aspect Identification.

关键词： Implicit aspect-based sentiment analysis Machine learning Supervised approaches WordNet K-nearest neighbors lesk algorithm

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An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第5期14卷 42-53页

作者： Benarafa, Halima Benkhalifa, Mohammed Akhloufi, Moulay Mohammed Univ Rabat Algorithms Networks Intelligent Syst & Software E Dept Comp Sci Fac Sci Rabat Morocco Univ Moncton Dept Comp Sci Percept Robot & Intelligent Machines Res Grp PRI Moncton NB Canada

Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers. SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers' choices and businesses' reputation. Aspect Based Sentiment Analysis (ABSA) is the task of categorizing text by aspect and identifying the sentiment attributed to it. Implicit Aspect Identification (IAI) is a subtask of ABSA. This paper aims to empirically investigate how external knowledge (e.g. WordNet) is integrated into SVM model to address some of its intrinsic issues when dealing with classification. To achieve this research goal, we propose an ap-proach to improve Support Vector Machines (SVM) model to deal with IAI. Using WordNet (WN) semantic relations, we suggest an enhancement for the SVM kernel computation. Experiments are conducted on three benchmark datasets of products, laptops, and restaurant reviews. The effects of our approach are examined and analyzed according to three criteria: (i) kernel function used, (ii) different experimental settings, and (iii) SVM behavior towards Overfitting and Underfitting. The research finding of our work is that the integration of external knowledge (e.g. WordNet) is experimentally proved to be significantly helpful to SVM classification for IAI and especially for addressing Overfitting and Underfitting that are considered as two of the main structural SVM issues. The empirical results demonstrate that our approach helps SVM (i) improve its performance for the three considered kernels and under different experimental settings, and (ii) deal better with Overfitting and Underfitting.

关键词： Implicit aspect-based sentiment analysis machine learning supervised approaches support vector machines wordnet lesk algorithm

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OntoOpinionMiner: An Opinion Mining algorithm for Drug Reviews 1

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5th International Conference on Soft Computing and its Engineering Applications (IcSoftComp)

作者： Srivastava, Rashi Deepak, Gerard Cent Univ Karnataka Kalaburagi Karnataka India Manipal Inst Technol Manipal Karnataka India

ISBN: (数字)9783031537318

ISBN: (纸本)9783031537301;9783031537318

The internet is now swamped with user data such as thoughts, comments, reviews, etc. due to the rise of social media in the modern day. This enables the creators to have a deeper understanding of the benefits and drawbacks of their creation totally based on the opinions of end users. Opinion mining has been one of the most studied fields along the lines of data mining and natural language processing in recent years. However, the methods to extract the important parts of the healthcare sector are still lacking in an abundance of literature. Patients constantly look for reviews of a specific drug from other users. In order to estimate the drug satisfaction rate among experienced patients, the proposed approach includes a novel three-fold feature selection and classification architecture. For improved outcomes, the Random-Forest Classifier and Long Short-Term Memory (LSTM) have been combined in the construction of this classifier. The outcomes obtained support the claim that this strategy is superior than traditional individual strategies. The proposed methodology has an average precision of 95.83.

关键词： Information Scent lesk algorithm LSTM Opinion Mining Random-Forest Classifier SimantoSim

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XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第7期13卷 502-516页

作者： Mandal, Kailash Pati Mukherjee, Prasenjit Chattopadhyay, Atanu Chakraborty, Baisakhi Natl Inst Technol Comp Sci & Engn Durgapur India Deshabandhu Mahavidyalaya BBA H & BCA H Dept Chittaranjan India

The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services. However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc. The rural villagers are not able to interact with the Relational Database Management system in their native language. Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words. This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain. The Healthcare Information database contains doctor, hospital and department details in the Bengali language. The proposed system provides support for the Bengali-speaking Indian rural population to efficiently fetch required information from the database. The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet. The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet. Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s). Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL). The simplified lesk method is utilized to resolve ambiguous Bengali phrases in this query processing system. The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.

关键词： Relational database management system (RDBMS) modified bengali WordNet lesk algorithm structured query language (SQL) natural language query

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Combination of information retrieval methods with lesk algorithm for Arabic word sense disambiguation

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ARTIFICIAL INTELLIGENCE REVIEW 2012年第4期38卷 257-269页

作者： Zouaghi, Anis Merhbene, Laroussi Zrigui, Mounir ISI Medenine UTIC Lab Unit Monastir Medenine Tunisia

In this paper, we propose to use Harman, Croft and Okapi measures with lesk algorithm to develop a system for Arabic word sense disambiguation, that combines unsupervised and knowledge based methods. This system must solve the lexical semantic ambiguity in Arabic language. The information retrieval measures are used to estimate the most relevant sense of the ambiguous word, by returning a semantic coherence score corresponding to the context that is semantically closest to the original sentence containing the ambiguous word. The lesk algorithm is used to assign and select the adequate sense from those proposed by the information retrieval measures mentioned above. This selection is based on a comparison between the glosses of the word to be disambiguated, and its different contexts of use extracted from a corpus. Our experimental study proves that using of lesk algorithm with Harman, Croft, and Okapi measures allows us to obtain an accuracy rate of 73%.

关键词： Arabic word sense disambiguation (AWSD) Unsupervised and incremental approach Knowledge based approach Information retrieval methods lesk algorithm

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Word Sense Disambiguation Using Swarm Intelligence: A Bee Colony Optimization Approach 17th

Word Sense Disambiguation Using Swarm Intelligence: A Bee Co...

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17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing)

作者： Kumar, Saket El Ariss, Omar Penn State Univ Dept Comp & Math Sci Harrisburg PA 17110 USA

ISBN: (纸本)9783319754772;9783319754765

Word Sense Disambiguation (WSD) is a problem of figuring out the correct sense of a word in a given context. We introduce an unsupervised knowledge-source approach for word sense disambiguation using a bee colony optimization algorithm that is constructive in nature. Our algorithm, using WordNet, optimizes the search space by globally disambiguating a document by constructively determining the sense of a word using the previously disambiguated words. Heuristic methods for unsupervised word sense disambiguation mostly give less importance to the context words while determining the sense of the target word. In this paper, we put more emphasis on the context and the part of speech of a word while determining its correct sense. We make use of a modified simplified lesk algorithm as a relatedness measure. Our approach is then compared with recent unsupervised heuristics such as ant colony optimization, genetic algorithms, and simulated annealing, and shows promising results. We finally introduce a voting strategy to our algorithm that ends up further improving our results.

关键词： Word sense disambiguation Bee colony optimization Semantic relatedness lesk algorithm Unsupervised WSD WordNet

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Recognition and Disambiguation of Polysemy Words in Entered Hindi Documents

Recognition and Disambiguation of Polysemy Words in Entered ...

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International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)

作者： Sajini, G. Kallimani, Jagadish S. MS Ramaiah Inst Technol Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781538623619

In this digital age, natural language is causing hindrance in the advancement of information technology revolution in India. There is a need to perform Natural Language Processing (NLP) using computer processing, so that computer based systems can be accessible through natural languages like Hindi. Therefore a language translator is very important tool to resolve this problem. One of the key challenges involved in the design of a language translator is Polysemy disambiguation. Polysemy means having two or more meanings to a single word. For language translation operations, it is crucial to find the right meaning of any given word in its context. This is known as Word Sense Disambiguation (WSD). Various resources and tools already exist for WSD in English language while regional languages have not been equally explored yet. This project has a user interface where the user enters a meaningful sentence in Hindi with polysemy word. The system identifies the polysemous word/s if any in the sentence and lists one or more meanings associated with the polysemous word. Using existing machine learning techniques the project identifies the right meaning of the polysemous word based on the given context.

关键词： Word Sense Disambiguation (WSD) Polysemy words lesk algorithm Shallow Parser Supervised Unsupervised and semi-supervised methods

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