Word Sense Disambiguation System Research Articles

Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation

Word Sense Disambiguation (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications. A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naïve Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars. Furthermore, the Assamese-Hindi transliteration system was merged with the baseline MT system for the translation of proper nouns. This study marks a remarkable contribution to Assamese NLP, which is a low computationally aware Indian language.

A Comprehensive Review of Word Sense Disambiguation Research in few Indian Languages: Implications for Educational Tools

Natural Languages are inherently ambiguous. Word Sense Disambiguation is one such problem where one word has multiple meaning depending upon the context in which it appears in the text. It can be considered as an intermediate step for many NLP applications like Machine Translation, Summarization, Query Processing, etc. Developing a Word Sense Disambiguation (WSD) system for the Gujarati language can have significant applications in the education sector. There are different approaches available for WSD which includes Supervised, Unsupervised and knowledge-based approaches. The work done is English language is extensive for this problem but for other regional languages more research needs to be done. This paper presents various works, mentioning their proposed method, datasets used, limitations and performance for some Indian languages like Hindi, Malayalam, Bengali, etc. The paper also enlists some general observations about existing approaches for word sense disambiguation. In the last section, the paper proposes a method to resolve WSD problem for Gujarati Language using Genetic Algorithm.

EnhancedBERT: A feature-rich ensemble model for Arabic word sense disambiguation with statistical analysis and optimized data collection

Accurate assignment of meaning to a word based on its context, known as Word Sense Disambiguation (WSD), remains challenging across languages. Extensive research aims to develop automated methods for determining word senses in different contexts. However, the literature lacks the presence of datasets generated for the Arabic language WSD. This paper presents a dataset comprising a hundred polysemous Arabic words. Each word in the dataset encompasses 3–8 distinct senses, with ten example sentences per sense. Some statistical operations are conducted to gain insights into the dataset, enlightening its characteristics and properties. Subsequently, a novel WSD approach is proposed to utilize similarity measures and find the overlap between contextual information and dictionary definitions. The proposed method uses the power of BERT, a pre-trained language model, to enable effective Arabic word disambiguation. In training, new features are integrated to improve the model's ability to differentiate between various senses of words. The proposed BERT models are combined to compose an ensemble model architecture to improve the classification performances. The performance of the WSD system outperforms state-of-the-art systems, achieving an approximate F1-score of 96 %. Statistical analyses are performed to evaluate the overall performance of the WSD approach by providing additional information on model predictions. A case study was implemented to test the effectiveness of WSD in sentiment analysis, a downstream task.

Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach

Machine Translation (MT) is a crucial application of Natural language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree (DT), Bayesian network, Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The range of accuracy for all the algorithms that were examined is between sixty-eight and eighty-four percent. To improve the accuracy of the AmbiF model, we have merged the DT, SVM, and Naïve Bayes (NB)-classifier approach. For testing the model, we have used the ten-fold cross-validation test method. The AmbiF model’s accuracy has been reported eighty-five percent. Comparing the AmbiF model to all other analyzed supervised ML classification algorithms, it has also demonstrated great precision, recall, and F-score. Waikato Environment for Knowledge Analysis (WEKA)’s ML-tool is used to analyze the algorithms and the AmbiF model.

Naïve Bayes Approach for Word Sense Disambiguation System With a Focus on Parts-of-Speech Ambiguity Resolution

Naïve Bayes Approach for Word Sense Disambiguation System With a Focus on Parts-of-Speech Ambiguity Resolution

Towards Developing Word Sense Disambiguation System for Kashmiri Language

Background: A word, phrase, sentence or other communication is “ambiguous” if interpreted in multiple ways. The process of assigning the correct meaning to a word with respect to its context is known as Word Sense Disambiguation (WSD). WSD is intended to be a very imperious problem in Natural Language Processing (NLP) that requires proper attention as it impacts the performance of various NLP applications.Objectives: In this paper first attempt is made to propose a supervised machine learning Kashmiri WSD system.Material & Methods: The dataset comprising of 500K tokens for this research study has been collected from different resources. A sense annotated corpus for fifty commonly used ambiguous Kashmiri words has been created using the manual annotation method. Kashmiri WordNet is used to extract senses for the target words. Decision-tree based classifier is trained using the features extracted from annotated corpus for carrying out WSD task. We have used context widow of ±3 to extract features that are used to train the classifier.Results: The proposed system is tested on all fifty target words and evaluation is carried using accuracy, precision, recall and F-1 measures. The proposed system reported 81.831% accuracy, 0.834 precision,0.816 recall and 0.824 F1-measure.Conclusions: This was the initial step towards developing the WSD system for Kashmir and it has shown good results. In the future we expect to use other algorithms to carry out this task with greater language coverage

Development of a XLM-Encoded Machine-Readable Dictionary for Yoruba Word Sense Disambiguation

The development of the disambiguation component of a Yorùbá to English machine translation system is hindered by several factors. One of these is the lack of machine readable sense inventory for ambiguous Yorùbá words. This study addressed the problem by developing a machine readable dictionary for ambiguous Yoruba verbs. To achieve this, ambiguous Yorba verbs and their translations were collected from existing bilingual dictionaries. The collected lexicons were transformed into machine readable format using the Extensible Markup Language (XML) Format. The accuracy of translation of the machine readable dictionary was evaluated using mean opinion score, with a score of 4.37 over the scale of 5. This study covered the total number of ninety-three (93) monosyllabic verbs with two hundred and forty-one (241) senses, which gives a coverage of 69.5% of the ambiguous monosyllabic verbs in Yoruba Language. The sense inventory was also used as a component of a Yoruba Word Sense Disambiguation system, and an accuracy of 94.6% was achieved. This study concludes that the digitized data can increase the accuracy of Word Sense Disambiguation component of a Yorùbá to English machine translation system.

Step-wise discriminative learning on uncertain annotations for word sense disambiguation

Uncertain annotations with sparsity and uncertainty widely exist for many natural language processing tasks. In this paper, a new word sense disambiguation system is proposed, which integrates rich features and distributed discrimination algorithm, and constructs a method for learning on fuzzy annotations, which can effectively learn from sparse and uncertain annotations. The algorithm considers the elementary components in ascending order concerning their ambiguity degrees, which learns a concept labeler with promising performance with only an inventory extracted from an ontology. Experiments show that the performance of the algorithm significantly outperforms the intuitive generative unsupervised method with the same linguistic resource.

Sentence Level Amharic Word Sense Disambiguation

Lexical ambiguity, phonological ambiguity, structural ambiguity, referential ambiguity, semantic ambiguity, and orthographic ambiguity were all types of Amharic ambiguity. The other ambiguities were out of this research because the study focuses on lexical-semantic, orthographic, and semantic ambiguities. Until now, some experts have been researching the Amharic word sense disambiguation system. Recent research, on the other hand, did not take into account antonym, troponymy, holonomy, and homonym relationships in the WordNet; this problem was overcome by this study. Using a Deep Learning method, we are developing an Amharic word sense disambiguation model. We use a design science research strategy to close the gap, starting with problem identification and concluding with final communication. 159 ambiguous words, 1214 synsets, and 2164 sentence datasets were used to create three distinct Deep Learning algorithms in three separate experiments. Using the given dataset, the overall performance of the model is measured using performance metrics in precision, F-measure, and confusion matrix. In this study, LSTM, CNN, and Bi-LSTM obtained 94 percent, 95 percent, and 96 percent accuracy respectively in the third experiment, based on performance measurement.

A metaheuristic with a neural surrogate function for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many tasks such as machine translation, information extraction, and semantic parsing. In this work, a new approach for WSD is proposed which uses a neural network as a surrogate fitness function in a metaheuristic algorithm. Also, a new method for simultaneous training of word and sense embeddings is proposed in this work. Accordingly, the node2vec algorithm is employed on the WordNet graph to generate sequences containing both words and senses. These sequences are then used along with paragraphs from Wikipedia in the word2vec algorithm to generate embeddings for words and senses at the same time. In order to address data imbalance in this task, sense probability distribution data extracted from the training corpus is used in the search process of the proposed simulated annealing algorithm. Furthermore, we introduce a new approach for clustering and mapping senses in the WordNet graph, which considerably improves the accuracy of the proposed method. In this approach, nodes in the WordNet graph are clustered on the condition that no two senses of the same word be present in one cluster. Then, repeatedly, all nodes in each cluster are mapped to a randomly selected node from that cluster, meaning that the representative node can take advantage of the training instances of all the other nodes in the cluster. Training the proposed method in this work is done using the SemCor dataset and the SemEval-2015 dataset has been used as the validation set. The final evaluation of the system is performed on SensEval-2, SensEval-3, SemEval-2007, SemEval-2013, SemEval-2015, and the concatenation of all five mentioned datasets. The performance of the system is also evaluated on the four content word categories, namely, nouns, verbs, adjectives, and adverbs. Experimental results show that the proposed method achieves accuracies in the range of 74.8 to 84.6 percent in the ten aforementioned evaluation categories which are close to and in some cases better than the state of the art in this task.

A Lemmatizer for Low-resource Languages: WSD and Its Role in the Assamese Language

The morphological variations of highly inflected languages that appear in a text impede the progress of computer processing and root word determination tasks while extracting an abstract. As a remedy to this difficulty, a lemmatization algorithm is developed, and its effectiveness is evaluated for Word Sense Disambiguation (WSD). Having observed its usefulness, lemmatizer is considered for developing Natural Language Processing tools for languages rich in morphological variations. Among various Indian highly inflected languages, Assamese, spoken by over 14 million people in the North-Eastern region of India, is also one of them. In this present work, after a detailed study on the possible transformations through which surface words are created from lemmas, we have designed an Assamese lemmatizer in such a manner that suitable reverse transformations can be employed on a surface word to derive the co-relative (similar) lemma back. And it has been observed that the lemmatizer is competent to deal with inflectional and derivational morphology in Assamese, and the same was evaluated on various Assamese articles extracted from the Assamese Corpus consisting of 50,000 surface words (excluding proper nouns), and the result that it yielded with 82% accuracy was quite encouraging and satisfying, as Assamese is a low-level language and no research work has been done in the Assamese language regarding the lemmatization of words. Considering the result obtained, the lemmatizer is then evaluated for Assamese WSD. For this purpose, 10 highly polysemous Assamese words are taken into account for sense disambiguation. We have also regarded varied WSD systems and observed that such systems enhance the effectiveness of all the WSD systems, which is statistically significant.

A novel word sense disambiguation approach using WordNet knowledge graph

Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human’s natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document’s main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms’ order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.

Role of Genetic Algorithm in Optimization of Hindi Word Sense Disambiguation

The Word Sense Disambiguation system (WSD) is widely used in many fields, including business, research, education, and government organizations. The availability of natural language data on the internet has grown in tandem with the rapid advancement of technology and the widespread use of the internet. Disambiguation is best tool for avoiding the problem of misperception. The most important feature of this approach is that words with multiple meanings of nouns can be easily disambiguated according to the sense of the sentences. As a result of incorporating all of these factors, This work will concentrate on the accuracy of adjectives and verbs. Many natural languages, including English, German, Arabic, Assamese, and others, have different methodologies proposed, however, work on Word Sense Disambiguation in Hindi is limited. For Hindi Word Sense Disambiguation, the proposed algorithm employs a genetic algorithm. The dynamic configuration window function, including the ambiguous terms left and right, is used. The central tenet of this method is that the target term and its surroundings share a common topic. With approximately 65.17%, 72%, and 74.1 %, respectively, the proposed work has compared to existing approaches such as graph-based approaches, association rules approaches, and probabilistic latent semantic analysis approaches. The proposed method has an accuracy of 80%, which indicating that it improves the existing Work by 8% in terms of accuracy and other parameters. Results show that the proposed work generates more accurate results for Hindi word sense disambiguation.

Improved Word Sense Determination in Malayalam using Latent Dirichlet Allocation and Semantic Features

Recent years have witnessed phenomenal developments worldwide in the field of NLP . But developments in Indian regional languages are very few compared to them. This work is a step towards the construction of a target word sense disambiguation system in Malayalam, which is the regional language of the state of Kerala, India. Word Sense Disambiguation/Determination refers to the task of correctly identifying the sense of an ambiguous word from its context. This is considered an AI-Complete problem in the field of Natural Language Processing . For this purpose, an exclusive corpus of 1,147 contexts of target ambiguous words has been created, which to the best of our knowledge is the first attempt in Malayalam. This work describes how the performance of an unsupervised LDA-based approach towards WSD could be improved using semantic features like synonyms and co-occurrence information.

Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation

Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the context and sense glosses of the target polysemy independently using encoders with the same structure. To obtain a better presentation in each encoder, we leverage the capsule network to capture different important information contained in multi-head attention. We finally choose the gloss representation closest to the context representation of the target word as its correct sense. We do experiments on English all-words WSD task. Experimental results show that our method achieves good performance, especially having an inspiring effect on disambiguating words of rare senses.

Word sense disambiguation based on context selection using knowledge-based word similarity

In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.

Exploring Resources in Word Sense Disambiguation for Marathi Language

Word Sense Disambiguation (WSD) is one of the most challenging problems in the research area of natural language processing. To find the correct sense of the word in a particular context is called Word Sense Disambiguation. As a human, we can get a correct sense of the word given in the sentence because of word knowledge of that particular natural language, but it is not an easy task for the machine to disambiguate the word. Developing any WSD system, it required sense repository and sense dictionary. It is very costly and time-consuming to build these resources. Many foreign languages have available these resources, that is why most of the foreign languages like English, German, Spanish etc lot of work is done in these Natural languages. When we look for Indian languages like Hindi, Marathi, Bengali etc. very less work is done. The reason behind this is resource-scarcity. In this paper, we majorly focus on Marathi Language Word Sense Disambiguation because of very less work is done in the Marathi Language as compared to Hindi and other Indian Languages. Our main objective is to provide information about various resources available for the Marathi language which will be helpful for researchers who wants to do work for Marathi WSD. This paper also gives a review on work done for Marathi Language WSD and its challenges and problems.

Multilingual Approach to Word Sense Disambiguation Using word2vec and Sense Embedding With Semantic Cloud

Word Sense Disambiguation(WSD) is an open problem in the area of Natural language processing. Many WSD systems uses knowledge based unsupervised approach as it bares us from using tagged corpus. But this approach depends extensively on knowledge. Enhancing the knowledge for ambiguity resolution is one of the important issues in WSD. Although Multilingual lexical resource is an asset for knowledge based systems(KBS), many KBS have not yield satisfactory results due to lack of sufficient information required for disambiguation. Multilingual information can help boost the performance of WSD in various languages. Combining lexical semantic information from different languages helps improve WSD task. We make use of Babel Net, a huge multilingual resource provider to attain the task and design a semantic word cloud to provide high performance disambiguation by imposing additional knowledge.

Train-O-Matic: Supervised Word Sense Disambiguation with no (manual) effort

Word Sense Disambiguation (WSD) is the task of associating the correct meaning with a word in a given context. WSD provides explicit semantic information that is beneficial to several downstream applications, such as question answering, semantic parsing and hypernym extraction. Unfortunately, WSD suffers from the well-known knowledge acquisition bottleneck problem: it is very expensive, in terms of both time and money, to acquire semantic annotations for a large number of sentences. To address this blocking issue we present Train-O-Matic, a knowledge-based and language-independent approach that is able to provide millions of training instances annotated automatically with word meanings. The approach is fully automatic, i.e., no human intervention is required, and the only type of human knowledge used is a task-independent WordNet-like resource.Moreover, as the sense distribution in the training set is pivotal to boosting the performance of WSD systems, we also present two unsupervised and language-independent methods that automatically induce a sense distribution when given a simple corpus of sentences. We show that, when the learned distributions are taken into account for generating the training sets, the performance of supervised methods is further enhanced. Experiments have proven that Train-O-Matic on its own, and also coupled with word sense distribution learning methods, lead a supervised system to achieve state-of-the-art performance consistently across gold standard datasets and languages. Importantly, we show how our sense distribution learning techniques aid Train-O-Matic to scale well over domains, without any extra human effort. To encourage future research, we release all the training sets in 5 different languages and the sense distributions for each domain of SemEval-13 and SemEval-15 at http://trainomatic.org.

Word sense disambiguation for Punjabi language using deep learning techniques

Word sense disambiguation (WSD) identifies the right meaning of the word in the given context. It is an indispensable and critical application for all the natural language processing tasks. In this paper, two deep learning techniques multilayer perceptron and long short-term memory (LSTM) have been individually inspected on the word vectors of 66 ambiguous Punjabi nouns for an explicit WSD system of Punjabi language. The inputs to the deep learning techniques are the simple word vectors derived directly from manually sense-tagged corpus of Punjabi language. The multilayer perceptron has outperformed the LSTM deep learning technique for WSD task of Punjabi language. Six traditional supervised machine learning techniques have also been tested on same dataset using unigram and bigram feature sets. A comparison between deep learning techniques and traditional six supervised machine learning techniques clearly indicates that the deep learning techniques using simple word vectors outperforms the earlier techniques.