A multiclassifier based approach for word sense disambiguation using Singular Value Decomposition (original) (raw)
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Machine Translation (MT) is a crucial application of (NLP) Natural language Processing. 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, Bayesian Network, Support Vect...
Word Sense Disambiguation by Machine Learning Approach: A Short Survey
There is a renewed interest in word sense disambiguation (WSD) as it contributes to various applications in natural language processing. Applications for which WSD is potentially an issue are: Machine Translation, Information Retrieval (IR), QA systems, Dialogue systems,etc. In this paper we survey vector-based methods for WSD in machine learning approache.
A New Supervised Learning Algorithm for Word Sense Disambiguation
1997
The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to nd a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best{ tting model at each level of model complexity. The Naive Mix utilizes this sequence of models to de ne a probabilistic model which is then used as a probabilistic classi er to perform word{sense disambiguation. The models in this sequence are restricted to the class of decomposable log{linear models. This class of models o ers a number of computational advantages. Experiments disambiguating twelve di erent words show that a Naive Mix formulated with a forward sequential search and Akaike's Information Criteria rivals established supervised learning algorithms such as decision trees (C4.5), rule induction (CN2) and nearest{neighbor classi cation (PEBLS).
Machine learning techniques for word sense disambiguation
2006
In the Natural Language Processing (NLP) community, Word Sense Disambiguation (WSD) has been described as the task which selects the appropriate meaning (sense) to a given word in a text or discourse where this meaning is distinguishable from other senses potentially attributable to that word. These senses could be seen as the target labels of a classification problem. That is, Machine Learning (ML) seems to be a possible way to tackle this problem.
A comparison between supervised learning algorithms for word sense disambiguation
Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning -, 2000
This paper describes a set of comparative experiments, including cross-corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNoW, Decision Lists, and Boosting. Two main conclusions can be drawn: 1) The LazyBoosting algorithm outperforms the other four state-of-theart algorithms in terms of accuracy and ability to tune to new domains; 2) The domain dependence of WSD systems seems very strong and suggests that some kind of adaptation or tuning is required for cross-corpus application.
An Insight into Word Sense Disambiguation Techniques
International Journal of Computer Applications, 2015
This paper presents various techniques used in the area of Word Sense Disambiguation (WSD). There are a number of techniques such as: Knowledge based approaches, which use the knowledge encoded in Lexical resources; Supervised Machine Leaning methods in which the classifier is made to learn from previously semantically annotated corpus; Unsupervised approaches that form cluster occurrences of words. Then there are also semi supervised approaches which use semi annotated corpus as reference data along with unlabeled data.
A Review of Literature on Word Sense Disambiguation
2012
Artificial intelligence (AI) has been a major research area in the later quarter of 20 th century and is likely to be even more so in the 21 st century. A key part of AI is Word Sense Disambiguation (WSD) which deals with choosing the correct sense of a word in the given text. All human languages have words with multiple meaning and selecting the intended sense is important. This paper briefly describes various methods presently used for WSD and their relative effectiveness. WSD applications currently find application in Information Retrieval, Information Extraction, Automated Answering Machine, Speech Reorganization, Machine Translation among many others. WSD has promise for the future in taking AI to the next higher level. Kywords: Natural Language Processing (NLP), Artificial Intelligence (AI), Word Sense Disambiguation (WSD), Knowledge Based Methods, Supervised/Unsupervised Methods.
Approaches for Word Sense Disambiguation - A Survey
International Journal of Recent Technology and Engineering, 2014
Word sense disambiguation is a technique in the field of natural language processing where the main task is to find the correct sense in which a word occurs in a particular context. It is found to be of vital help to applications such as question answering, machine translation, text summarization, text classification, information retrieval etc. This has resulted in excessive interest in approaches based on machine learning which performs classification of word senses automatically. The main motivation behind word sense disambiguation is to allow the users to make ample use of the available technologies because ambiguities present in any language provide great difficulty in the use of information technology as words in human language that occur in a particular context can be interpreted in more than one way depending on the context. In this paper we put forward a survey of supervised, unsupervised and knowledge based approaches and algorithms available in word sense disambiguation (WSD). Index Terms-Machine readable dictionary, Machine translation, Natural language processing, Wordnet, Word sense disambiguation.
An Empirical Analysis of Word Sense Disambiguation through Machine Learning Approaches
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
The procedure to identify the appropriate meaning for the particular word in an ambiguous statement is considered as Word Sense Disambiguation. It is a complicated problem since it necessitates the utilization of information from a variety of sources. Since the start of machine learning, a significant amount of time and effort has been devoted to overcoming this challenge, and the work is currently ongoing. In WSD, a variety of methodologies were employed and executed on a variety of corpora representing practically all languages. WSD algorithms are grouped into three groups in this paper: Supervised algorithms, unsupervised algorithms and knowledge-based algorithms. Every subcategory will be examined thoroughly, with details elaborated for nearly all of the algorithms within each area. As a result, work samples for every technique were selected based on the language being used, the corpora being used, and other considerations. Each method's advantages and disadvantages were meticulously documented. Some of these strategies have limits in certain scenarios, and our work will assist scientists in the fields of machine learning in selecting the most appropriate algorithms to tackle their specific problem in WSD. When comparing the works that have been used and indeed the procedures that were employed, it is possible to notice the distinctiveness of the piece of work that was created. As a result of this research, it was observed that (i) size of the dataset has an considerable impact on algorithm's performance, (ii) some methodologies provide high performance accuracy for one language where as it gives low performance for some other, (iii) a few of these methodologies can be run quickly but with a limitation on accuracy, and (iv) the large number among those methodologies have been implemented successfully for a wide range of different languages.