Disambiguation Research Articles

Two linguistic levels of lexical ambiguity and a unified categorical representation

Lexical disambiguation is one of the oldest problems in natural language processing. There are three main types of lexical ambiguity: part-of-speech ambiguity, homonymy, and polysemy, typically divided into two tasks in practice. While this division suffices for engineering purposes, it does not align well with human intuition. In this article, I use lexical ambiguity as a representative case to demonstrate how insights from theoretical linguistics can be helpful for developing more human-like meaning and knowledge representations in natural language understanding. I revisit the three types of lexical ambiguity and propose a structured reclassification of them into two levels using the theoretical linguistic tool of root syntax. Recognizing the uneven expressive power of root syntax across these levels, I further translate the theoretical linguistic insights into the language of category theory, mainly using the tool of topos. The resulting unified categorical representation of lexical ambiguity preserves rootsyntactic insights, has strong expressive power at both linguistic levels, and can potentially serve as a bridge between theoretical linguistics and natural language understanding.

EnhancedBERT: A python software tailored for arabic word sense disambiguation

EnhancedBERT: A python software tailored for arabic word sense disambiguation

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Additional Diverse Techniques for Improvising Lesk Algorithm to Enhance Manipuri Word Sense Disambiguation

Additional Diverse Techniques for Improvising Lesk Algorithm to Enhance Manipuri Word Sense Disambiguation

Naïve Bayes classifier for Kashmiri word sense disambiguation

Naïve Bayes classifier for Kashmiri word sense disambiguation

Evaluating Word Sense Disambiguation Techniques for Punjabi Language: A Comparative Analysis

Word Sense Disambiguation (WSD) is a fundamental task in natural language processing (NLP) that focuses on determining the precise meaning of a word by analyzing its contextual usage.This paper presents a comprehensive analysis of various WSD techniques applied to the Punjabi language, including supervised, unsupervised, and knowledge-based methods. We compare the accuracy, performance, benefits, drawbacks, and resource requirements of these techniques.The study aims to provide a detailed overview of the state of WSD for Punjabi, with visual representations such as tables and graphs to illustrate comparative performance. Key Words: Word Sense Disambiguation, Punjabi Language, Natural Language Processing, Supervised Learning, Unsupervised Learning, Knowledge-Based Approach

Enhancing Word Sense Disambiguation for Amharic homophone words using Bidirectional Long Short-Term Memory network

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fidel's ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (both pronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, which tackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m'hur) and ምሑር(m'hur), በአል(be'al) and በዢል(be'al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), decision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limited datasets.

Clustering for Clarity: Improving Word Sense Disambiguation through Multilevel Analysis

In natural language processing, a critical activity known as word sense disambiguation (WSD) seeks to ascertain the precise meaning of an ambiguous wordin context. Traditional methods for WSD frequently involve supervised learning methods and lexical databases like WordNet. However, these methods fallshort in managing word meaning complexity and capturing fine-grained differences. In this paper, for increasing the precision and granularity of word sensedisambiguation we proposed multilevel clustering method that goes deeper in the nested levels as locate groups of linked context words and categorize themaccording to their word meanings. With this method, we can more effectively manage polysemy and homonymy as well as detect minute differences in meaning. An actual investigation of the SemCor corpus demonstrates the performance score of multilevel clustering in WSD. This proposed method successfullyseparated clusters and groups context terms according to how semantically related they are, producing improved disambiguation outcomes. A more detailedknowledge of word senses and their relationships may be obtained thanks to the clustering process, which makes it possible to identify smaller clusters inside larger clusters. The outcomes demonstrate how multilevel clustering may enhance the granularity and accuracy of WSD. Our solution overcomes the drawbacks of conventional approaches and provides a more fine-grained representation of word senses by combining clustering algorithms.

Word Sense Disambiguation for Indic Language using Bi-LSTM

Word Sense Disambiguation for Indic Language using Bi-LSTM

Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model

Word sense disambiguation (WSD) remains a persistent challenge in the natural language processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library demonstrates suboptimal accuracy. In this research article, we propose an innovative methodology and an open-source framework that effectively addresses the challenges of WSD by optimizing memory usage without compromising accuracy. Our system seamlessly integrates WSD into NLP tasks, offering functionality similar to that provided by the NLTK library. However, we go beyond the existing approaches by introducing a novel idea related to WSD. Specifically, we leverage deep neural networks and consider the language patterns learned by these models as the new gold standard. This approach suggests modifying existing semantic dictionaries, such as WordNet, to align with these patterns. Empirical validation through a series of experiments confirmed the effectiveness of our proposed method, achieving state-of-the-art performance across multiple WSD datasets. Notably, our system does not require the installation of additional software beyond the well-known Python libraries. The classification model is saved in a readily usable text format, and the entire framework (model and data) is publicly available on GitHub for the NLP research community.

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Enhancing Academic Integrity: An Analysis of Advanced Techniques for Plagiarism Detection using LESK, Word Sense Disambiguation, and SVM

Modified lesk algorithm for word sense disambiguation in Bengali

Modified lesk algorithm for word sense disambiguation in Bengali

A Dataset for Evaluating Contextualized Representation of Biomedical Concepts in Language Models

Due to the complexity of the biomedical domain, the ability to capture semantically meaningful representations of terms in context is a long-standing challenge. Despite important progress in the past years, no evaluation benchmark has been developed to evaluate how well language models represent biomedical concepts according to their corresponding context. Inspired by the Word-in-Context (WiC) benchmark, in which word sense disambiguation is reformulated as a binary classification task, we propose a novel dataset, BioWiC, to evaluate the ability of language models to encode biomedical terms in context. BioWiC comprises 20’156 instances, covering over 7’400 unique biomedical terms, making it the largest WiC dataset in the biomedical domain. We evaluate BioWiC both intrinsically and extrinsically and show that it could be used as a reliable benchmark for evaluating context-dependent embeddings in biomedical corpora. In addition, we conduct several experiments using a variety of discriminative and generative large language models to establish robust baselines that can serve as a foundation for future research.

Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. Keywords: Hybrid Genetic Algorithm, Trained Bayesian Ensemble, Short Messages Spam Detection Odiakaose, C. Emordi, F. Ejeh, P., Ashioba, N., Odeh, C., Attoh, O. & Azaka, M. (2024): Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 37-52. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P4

Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation

Natural language processing includes a subfield called word sense disambiguation, which focuses mostly on words that might have several meanings. Polysemous terms are also referred to as confusing phrases in some circles. The performance of word sense disambiguation depends on how effectively the ambiguous word is recognized by the machine. The discussed word embedding model for the ambiguous words represents the words from the document space to vector space with no data loss. The most identified challenge of ambiguous word representation is the features. The selection and representation of ambiguous words with respect to the features is the tedious task of word embedding. The discussed word embedding model uses countable features of available context for disambiguation. The proposed model is implemented for ambiguous words with context information. The available context of ambiguous/polysemous words is used for disambiguation. The unavailability of the context is the challenge in this model. The Recurrent Neural Network with Large Small Term Memory is used for the classification. The output of the RNN-LSTM is the sense values which are further mapped with the freely available lexical resource WordNet for retrieving the correct sense(meaning).

Homonym Detection Using WordNet and Modified Lesk Approach for English Language

Word sense disambiguation (WSD) is a basic and persistent problem that has existed since its inception in the natural language processing (NLP) area. The process of determining the accurate meaning of a word within a specific context is referred to as word sense disambiguation, commonly known as WSD. In NLP, a single word can have two or more meanings, with each meaning being distinguished by its context. This is known as word polysemy. Its applications span a wide range of fields, such as question answering systems, machine translation, information retrieval (IR) etc. Ontology and NLP are still struggling with ambiguity. Homonyms, which are ubiquitous in most languages, are words that have the same spelling but a different meaning. This method's fundamental premise is to select the appropriate sense by comparing a word's context in a sentence to contexts generated from WordNet. The primary goal of this study is to employ WordNet and the Lesk algorithm for WSD. After the algorithm was put into practice and tested on a collection of sentences that included ambiguous words, the synset was able to determine the proper interpretation for most of the sentences. The Lesk algorithm relies on finding the highest number of shared words (maximum overlap) between a word’s context, prepositions and the definitions for its different meanings(glosses). This approach helps in identifying the most accurate interpretation for a given word within a specific context. According to experimental findings, the suggested strategy considerably boosts performance while identifying homonyms.

Quantum Interference Model for Semantic Biases of Glosses in Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to determine the meaning of the target word according to the given context. Currently, a single representation enhanced by glosses from different dictionaries or languages is used to characterize each word sense. By analyzing the similarity between glosses of the same word sense, we find semantic biases among them, revealing that the glosses have their own descriptive perspectives. Therefore, the traditional approach of integrating all glosses by a single representation results in failing to present the unique semantics revealed by the individual glosses. In this paper, a quantum superposition state is employed to formalize the representations of multiple glosses of the same word sense to reveal their distributions. Furthermore, the quantum interference model is leveraged to calculate the probability that the target word belongs to this superposition state. The advantage is that the interference term can be regarded as a confidence level to guide word sense recognition. Finally, experiments are performed under standard WSD evaluation framework and the latest cross-lingual datasets, and the results verify the effectiveness of our model.

Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm.

To help non-native English speakers quickly master English vocabulary, and improve reading, writing, listening and speaking skills, and communication skills, this study designs, constructs, and improves an English vocabulary learning model that integrates Spiking Neural Network (SNN) and Convolutional Long Short-Term Memory (Conv LSTM) algorithms. The fusion of SNN and Conv LSTM algorithm can fully utilize the advantages of SNN in processing temporal information and Conv LSTM in sequence data modeling, and implement a fusion model that performs well in English vocabulary learning. By adding information transfer and interaction modules, the feature learning and the timing information processing are optimized to improve the vocabulary learning ability of the model in different text contents. The training set used in this study is an open data set from the WordNet and Oxford English Corpus data corpora. The model is presented as a computer program and applied to an English learning application program, an online vocabulary learning platform, or a language education software. The experiment will use the open data set to generate a test set with text volume ranging from 100 to 4000. The performance indicators of the proposed fusion model are compared with those of five traditional models and applied to the latest vocabulary exercises. From the perspective of learners, 10 kinds of model accuracy, loss, polysemy processing accuracy, training time, syntactic structure capturing accuracy, vocabulary coverage, F1-score, context understanding accuracy, word sense disambiguation accuracy, and word order relation processing accuracy are considered. The experimental results reveal that the performance of the fusion model is better under different text sizes. In the range of 100-400 text volume, the accuracy is 0.75-0.77, the loss is less than 0.45, the F1-score is greater than 0.75, the training time is within 300s, and the other performance indicators are more than 65%; In the range of 500-1000 text volume, the accuracy is 0.81-0.83, the loss is not more than 0.40, the F1-score is not less than 0.78, the training time is within 400s, and the other performance indicators are above 70%; In the range of 1500-3000 text volume, the accuracy is 0.82-0.84, the loss is less than 0.28, the F1-score is not less than 0.78, the training time is within 600s, and the remaining performance indicators are higher than 70%. The fusion model can adapt to various types of questions in practical application. After the evaluation of professional teachers, the average scores of the choice, filling-in-the-blank, spelling, matching, exercises, and synonyms are 85.72, 89.45, 80.31, 92.15, 87.62, and 78.94, which are much higher than other traditional models. This shows that as text volume increases, the performance of the fusion model is gradually improved, indicating higher accuracy and lower loss. At the same time, in practical application, the fusion model proposed in this study has a good effect on English learning tasks and offers greater benefits for people unfamiliar with English vocabulary structure, grammar, and question types. This study aims to provide efficient and accurate natural language processing tools to help non-native English speakers understand and apply language more easily, and improve English vocabulary learning and comprehension.

Application of the transformer model algorithm in chinese word sense disambiguation: a case study in chinese language

This study aims to explore the research methodology of applying the Transformer model algorithm to Chinese word sense disambiguation, seeking to resolve word sense ambiguity in the Chinese language. The study introduces deep learning and designs a Chinese word sense disambiguation model based on the fusion of the Transformer with the Bi-directional Long Short-Term Memory (BiLSTM) algorithm. By utilizing the self-attention mechanism of Transformer and the sequence modeling capability of BiLSTM, this model efficiently captures semantic information and context relationships in Chinese sentences, leading to accurate word sense disambiguation. The model’s evaluation is conducted using the PKU Paraphrase Bank, a Chinese text paraphrase dataset. The results demonstrate that the model achieves a precision rate of 83.71% in Chinese word sense disambiguation, significantly outperforming the Long Short-Term Memory algorithm. Additionally, the root mean squared error of this algorithm is less than 17, with a loss function value remaining around 0.14. Thus, this study validates that the constructed Transformer-fused BiLSTM-based Chinese word sense disambiguation model algorithm exhibits both high accuracy and robustness in identifying word senses in the Chinese language. The findings of this study provide valuable insights for advancing the intelligent development of word senses in Chinese language applications.