Resources for Nepali Word Sense Disambiguation

2020

Hindi is the widely used spoken language in the Indian subcontinent, and is used by more than 260 million Indians citizens. Indian governments has many digital initiatives to serve Indian citizen better, hence Hindi language becomes one of the important languages to serve Indian citizen. The Government initiatives are like smart city, Hospital Services, Common Service Centers, Digital Payment Ecosystem, Pensioners Scheme, Digital Locker and many more. These all initiative are served using mobile and web based applications, which citizens can access easily instead of visiting various government departments. To serve the large Hindi speaking population, it is necessary to handle the ambiguous words which have multiple connotations in any natural language processing task. In this paper, word sense disambiguation for Hindi language is proposed. Proposed method makes use of Lesk algorithm to disambiguate the Hindi words. Novel scoring method is used to assign a sense score to each token ...

IEEE, 2014

Word sense disambiguation (WSD) can be defined as a task that focuses on estimating the right sense of a word in its context. It is important as a pre-processing step in information extraction, machine translation, question answering and many other natural language processing tasks. Ambiguity in Word Sense arises when a particular word has more than one possible sense. Finding the correct sense requires thorough knowledge regarding words. This information of words is often derived from the sources such as words appearing in the context of the target word, part of speech information of the words in the neighbour, syntactical relations and local collocations. Our main aim in this paper is to develop an automatic system for WSD in Assamese using a Naive Bayes classifier. This is the first work to the best of our knowledge on developing an automatic WSD system for Assamese language. Assamese, the main language of most of the people in NorthEastern part of India is a morphologically very rich language. In Assamese WSD is a challenging task because a word can behave differently when combined with a suffix or a sequence of suffixes to have an entirely different sense. WSD often makes use of lexical resources such as WordNet, lexicon, annotated or unannotated corpora etc for its process of disambiguation.

2018

Word Sense Disambiguation has been a research area since the evolution of Natural Language processing. Most of the languages have ambiguous words. Resolution to these ambiguous words is a very importance task in developing any tool for natural language processing which otherwise hampers the efficiency of the developed systems. The accuracy of the output generated depends on the sense of the context a word is being used in a sentence. Word Sense Disambiguation is used for resolving the ambiguous words. This paper presents a brief introduction to Word Sense Disambiguation, an overview of the approaches that can be used solving ambiguity and work done for various Indian languages.

Machine Translation, 2000

Word Sense Disambiguation (WSD) is defined as the task of finding the correct sense of a word in a specific context. This is crucial for applications like Machine Translation and Information Extraction. While the work on automatic WSD for English is voluminous, to our knowledge, this is the first attempt for an Indian language at automatic WSD. We make use

Sadhana (2019)44:181, 2019

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naı¨veNaı¨ve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment.

Ambiguity is the big challenge in computational linguistics. Word sense disambiguation (WSD) is a natural classification problem. It is task to disambiguate the meaning of the word, which word has multiple senses. In natural language processing WSD is the crucial and challenging task in many applications, Such as machine translation, question answering system, multi-document summarization, information extraction and language generation.Marathi Language is predominantly spoken by the Marathi people of Maharashtra. It is an Indo-Aryan Language and officially uses in Maharashtra and Goa State. It is one of the twenty two scheduled languages of India. Lots of work is carried out for foreign languages using various technique of WSD as compare to Indian Languages. This paper is majorly focused on Marathi language word sense disambiguation. We discuss different techniques of WSD to disambiguate the Marathi language words.

The problem underlying this research is to solve word sense disambiguation problem for Urdu language text. Although the problem is well-studied for English language text, the work on Urdu is still in infancy. Since Urdu is a morphologically a rich language and hence very sparse, it may not be guaranteed that the methods that performance better for English language may also be effective for Urdu. The goal of this research is to propose a method for word sense disambiguation of ambiguous Urdu words and develop a prototype system. We have proposed a two-stage method where the first stage is detection phase and second stage is identification phase. The detection task is performed by maintaining a lexicon of ambiguous words. The identification problem is posed as a classification problem where a classifier is trained for each ambiguous word by developing a word sense disambiguation specific test collection. Two well-studied text classification algorithms have been analyzed for classification: support vector machine and Naïve Bayes. The results have shown that support vector machine has an advantage due to it intrinsic ability to handle sparse data and less sensitivity to high dimensionality of dataset. I. INTROUDCTION Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.. This extraction is basically applied on structured and semi-structured textual data that is readable to machine. However, the techniques of automatic annotation and content extraction from multimedia are also considered in IE. In IE, main goal is to automatically extract entities from data on the web, finding the relationships among entities and describing attributes of entities. The extracted knowledge enables much richer forms of queries on the abundant unstructured sources than possible with keyword searches alone. Another objective of IE is to represent the unstructured text in a form useful to derive inferences that are based on the logical content found in the input data. Assigning a correct sense automatically to a word that has more than one meaning is referred as word sense disambiguation (WSD). Many words have more than one meaning having correct sense in a particular context for which WSD technique is introduced. Word sense disambiguation is a process to resolve the ambiguity and rectify the use of word in a specific frame of reference [1]. Human are good to identify the correct sense of word with the help of context easily. WSD is a domain that deals with modeling this ability of humans into computers. Frequently it is noticed that some words may have multiple meanings according to their use as parts Of speech (POS). In the following example same word (bear) gives us a sense of verb and noun in two different sentences.

WSD is a Technique used to find the correct meaning of a given word in any human language. Each human language has a problem called ambiguity of a word. To finds the correct meaning of any ambiguous word is easy for human but for a machine it is great issues No of work has done on WSD but not enough in Hindi language. My objective is to provide the training to the system so that it can easily find the correct meaning of the any ambiguous word in Hindi language .For this purpose I simple used a one existing technique name modified lesk approach and give its output to the SVM to get the better result and show that SVM is better in compare to modified Lesk Approach, In this paper I simply take nine Hindi ambiguous words and three different databases to show the result.

Word Sense Disambiguation (WSD) is the process of identifying the proper sense of an ambiguous word depending on the particular context. It is to find the accurate sense si among the set of senses {s1, s2, …, sn}. This task was motivated by its interpretation in various Natural Language Processing (NLP) applications like IR, MT, QA, TC, SP etc. In this paper, machine learning technique - Naïve Bayes Classifier was used for automatic disambiguation task. Training data was prepared with sense annotated features. For preparing sense annotated data we took help of the sense inventory. Currently, about 160 ambiguous words are present in the sense inventory derived from 18K and 25K words from Assamese Corpus and WordNet. The system is implemented in two phases. In the first phase, a total of 2.7K sense annotated training data and 800 test data were taken and a result of 71% accuracy was found. Analyzing the result depicts that accuracy improves as the training data size gradually increases and by the learned model generated in the previous iteration. In second phase we manually validate the outcomes of first-phase and we add those clean sense tagged data to previous training data set. Than we train our system with our incresing training data (3.5K) which enhance the result accuracy. An iterative learning is adopted by the system and more accuracy of 7% is achieved. This paper aims to implement Assamese WSD system by NB classifier using lexical features and enhancement of the baseline method turns out in improving the classifier accuracy to 78%.

Procedia Computer Science, 2015

Semantic relations play an important role in resolving the ambiguity of a polysemous word. This paper investigates the role of hypernym, hyponym, holonym and meronym relations in Hindi Word Sense Disambiguation. In this work, we have considered five different cases: all relations, hypernym and hyponym, hypernym, hyponym and holonym. No semantic relations are used in the baseline. We obtained an overall improvement of 12.09% in precision over the baseline for the case when all relations are considered. The maximum improvement for single semantic relation was obtained using hyponym which resulted in an overall improvement of 9.86% in precision.