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DIFFERENT TECHNIQUES IMPLEMENTED IN GURUMUKHI WORD SENSE DISAMBIGUATION

DIFFERENT TECHNIQUES IMPLEMENTED IN GURUMUKHI WORD SENSE DISAMBIGUATION

Ambiguity is the quality of being open to more than one interpretation. All the Natural Languages known to us are full of ambiguous words i.e. the words have more than one meaning and this can be understood in the context in which they are being used. But as humans know in which context the particular word is being used, they understand the zest of it. Consider the following example:

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Study of Hindi Word Sense Disambiguation   Based on Hindi WorldNet

Study of Hindi Word Sense Disambiguation Based on Hindi WorldNet

Our paper is base on the Shallow approach methodology. The different types of Shallow approaches of WSD are: Supervised methods, Semi-supervised, Unsupervised methods, Hybrid approach. Supervised Techniques: The learning here perform in supervision. Let us take the example of the learning process of a small child. The child doesn’t know how to read/write. He/she is being taught by the parents at home and then by their teachers in school. The children are trained and modules to recognize the alphabets, numerals, etc. Their each and every action is supervised by the teacher. Actually, a child works on the basis of the output that he/she has to produce. Similarly, a word sense disambiguation system is learned from a representative set of labeled instances drawn from same distribution as test set to be used. In supervised learning, it is assumed that the correct (target) output values are known for each Input. So, actual output is
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A word sense disambiguation corpus for Urdu

A word sense disambiguation corpus for Urdu

various WSD techniques used in this study. Table 8 shows averaged accuracy scores obtained on three PoS categories (nouns, verbs, and adjectives) when four different WSD approaches are applied. In these tables, “Technique” refers to the WSD feature extraction approach used to identify the proper sense of a word in a particular sentence. “ML Algorithm” refers to the Machine Learning algorithms used for training and testing models based on features extracted using WSD approaches. PoS-1x1 means that a feature vector is formed by con- sidering the PoS tag of one word from the left, and one word from right side of target word. Similarly, PoS-2x2 means that feature vectors are formed by considering the PoS tags of two words from the left, and two words from right side of target word and so on. “WE-100” means the 100 neighboring words (from trained WE model) of target word are used to form a feature vector, “WE-200” means the 200 words (from the trained WE model) neighboring the target word are used and so on. “SVM” refers to Support Vector Machine clas- sifier. “KNN” refers to K-Nearest Neighbors classifier. “ID3” refers to Iterative Dichotomiser 3 classifier.
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Word Sense Disambiguation Using Context Clustering
   Pelja Paul.N, Binu R, Dibin Joseph  Abstract PDF  IJIRMET160405003

Word Sense Disambiguation Using Context Clustering Pelja Paul.N, Binu R, Dibin Joseph Abstract PDF IJIRMET160405003

Resolving ambiguity of words for getting the correct sense of the word in the context is word sense disambiguation (WSD) . WSD have a different context model for each individual word. Discriminate the word meanings based on information found in unannotated corpora is unsupervised WSD. One of the unsupervised WSD approaches is Context Clustering. Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. The context Clustering method is based on clustering techniques in which first context vectors are created and then they will be grouped into clusters to identify the meaning of the word. Here, context vectors created from different contexts. Then grouped this context vectors into different clusters. Then give a WSD context as input , it mapped into the related cluster. Predicted the context belongs to which cluster. It can be applied so many natural language applications such as news classification, email sorting etc.
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Word Sense Disambiguation by Relative Selection

Word Sense Disambiguation by Relative Selection

He indicated that a drawback of his method is on the ambiguous relative: just one sense of the ambiguous relative is usually related to a target word but the other senses of the ambiguous relatives are not. Hence, a collection of example sentences of the ambiguous relative includes the example sentences irrelevant to the target word, which prevent WSD systems from collecting correct WSD information. For example, an ambiguous word rail is a relative of a meaning bird of a target word crane at WordNet, but the word rail means railway for the most part, not the meaning related to bird. Therefore, most of the example sentences of rail are not helpful for WSD of crane. His method has another problem in disambiguating senses of a large number of target words because it requires a great amount of time and storage space to collect example sentences of relatives of the target words.
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A Topic Model for Word Sense Disambiguation

A Topic Model for Word Sense Disambiguation

Because the Dirichlet smoothing factor in part determines the topics, it also affects the disam- biguation. Figure 4 shows the modal disambigua- tion achieved for each of the settings of S = {0.1, 1, 5, 10, 15, 20}. Each line is one setting of K and each point on the line is a setting of S. Each data point is a run for the Gibbs sampler for 10,000 iterations. The disambiguation, taken at the mode, improved with moderate settings of S, which sug- gests that the data are still sparse for many of the walks, although the improvement vanishes if S dom- inates with much larger values. This makes sense, as each walk has over 100,000 parameters, there are fewer than 100,000 words in S EM C OR , and each
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Similarity based Word Sense Disambiguation

Similarity based Word Sense Disambiguation

4 The weight of a w o r d estimates its expected contribution to the disambiguation task and is a product of several factors: the frequency of the w o r d in the corpus; its frequency in[r]

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Domain Kernels for Word Sense Disambiguation

Domain Kernels for Word Sense Disambiguation

We will demonstrate that using DMs induced from unlabeled corpora is a feasible strategy to in- crease the generalization capability of the WSD al- gorithm. Our system far outperforms the state-of- the-art systems in all the tasks in which it has been tested. Moreover, a comparative analysis of the learning curves shows that the use of DMs allows us to remarkably reduce the amount of sense-tagged examples, opening new scenarios to develop sys- tems for all-words tasks with minimal supervision.

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A New Approach to Word Sense Disambiguation

A New Approach to Word Sense Disambiguation

A New Approach to Word Sense Disambiguation A N e w A p p r o a c h t o W o r d S e n s e D i s a m b i g u a t i o n Rebecca Bruce and Janyce Wiebe The Computing Research Lab New Mexico State Univers[.]

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Error Driven Word Sense Disambiguation

Error Driven Word Sense Disambiguation

Error Driven Word Sense Disambiguation Error D r i v e n W o r d S e n s e D i s a m b i g u a t i o n L u c a D i n i a n d V i t t o r i o D i T o m a s o F r 6 d 6 r i q u e S e g o n d C E L I X e[.]

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Error Driven Word Sense Disambiguation

Error Driven Word Sense Disambiguation

In the following sections we describe i the resources we used Penn Tree Bank, 45 upper level WordNet tags; ii the experiment we ran using rule induction techniques on functional relation[r]

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Personalizing PageRank for Word Sense Disambiguation

Personalizing PageRank for Word Sense Disambiguation

In (Navigli and Lapata, 2007), the authors per- form a two-stage process for WSD. Given an input context, the method first explores the whole LKB in order to find a subgraph which is particularly relevant for the words of the context. Then, they study different graph-based centrality algorithms for deciding the relevance of the nodes on the sub- graph. As a result, every word of the context is attached to the highest ranking concept among its possible senses. The Spr method is very similar to (Navigli and Lapata, 2007), the main differ- ence lying on the initial method for extracting the context subgraph. Whereas (Navigli and Lapata, 2007) apply a depth-first search algorithm over the LKB graph —and restrict the depth of the subtree to a value of 3—, Spr relies on shortest paths be- tween word synsets. Navigli and Lapata don’t re- port overall results and therefore, we can’t directly compare our results with theirs. However, we can see that on a PoS-basis evaluation our results are consistently better for nouns and verbs (especially the Ppr w2w method) and rather similar for adjec- tives.
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Word Sense Disambiguation with Multilingual Features

Word Sense Disambiguation with Multilingual Features

Our baseline consists of the predictions made by a majority class learner, which labels all examples with the predominant sense encountered in the training data. 8 Note that the most frequent sense baseline is often times difficult to surpass because many of the words exhibit a disproportionate usage of their main sense (i.e., higher than 90%), such as the noun bass or the verb approve. Despite the fact that the majority vote learner provides us with a supervised baseline, it does not take into consideration actual features pertaining to the instances. We therefore introduce a second, more informed baseline that relies on binary-weighted features extracted from the English view of the datasets and we train a multinomial Na¨ıve Bayes learner on this data. For every word included in our datasets, the binary-weighted Na¨ıve Bayes learner achieves the same or higher accuracy as the most frequent sense baseline.
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Supervised Approach to Word Sense Disambiguation

Supervised Approach to Word Sense Disambiguation

1.Ariel Raviv and Shaul Markovitch introduce Concept-Based Disambiguation (CBD),[1] a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast encyclopedic resource, thus enriching the disambiguation process with large amounts of domain-specific knowledge. In such concept-based spaces, more comprehensive measures can be applied in order to pick the right sense. Additionally, they introduce a novel representation scheme, denoted anchored representation, that builds a more specific text representation associated with an anchoring word. We evaluate our framework and show that the anchored representation is more suitable to the task of word sense disambiguation(WSD).
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Chinese Word Sense Disambiguation based on Context Expansion

Chinese Word Sense Disambiguation based on Context Expansion

We encounter two problems when expanding the contextual word with synonyms. The first problem is that not all the synonyms are suitable for generating training data. For example, contextual word “清楚” has synonyms such as “清晰”,“明晰”,“历历” and “不可磨灭” in dictionary. It is obvious that “历历” and “不可磨灭” should not be added into the expanded synonyms set, since the collocations of those synonyms with ambiguous word are rarely occur in large-scale corpus. In addition, the contextual words are not monotonous in most cases, and we do not know which sense of the ambiguous word should be expanded by synonyms. For example , Chinese word “可 以” has three meanings in dictionary. They are “不错”,“认可” and “可” respectively. Which sense should be expanded by synonyms in order to generate appropriate training data? To solve the above problems, we exploit word collocation relationship to restrict expansion of synonyms, i.e., only synonyms co-occurrence with ambiguous word that exceeded a certain number are used to train classifier. This strategy can not only filter out uncommonly used collocations, but also solve the problem of noise caused by ambiguity of contextual word. The collocation parameter threshold
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Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries

Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries

Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Diction[.]

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Word Sense Disambiguation vs  Statistical Machine Translation

Word Sense Disambiguation vs Statistical Machine Translation

The fourth voting model is a Kernel PCA-based model (Wu et al., 2004). Kernel Principal Compo- nent Analysis (KPCA) is a nonlinear kernel method for extracting nonlinear principal components from vector sets where, conceptually, the n-dimensional input vectors are nonlinearly mapped from their original space R n to a high-dimensional feature space F where linear PCA is performed, yielding a transform by which the input vectors can be mapped nonlinearly to a new set of vectors (Sch¨olkopf et al., 1998). WSD can be performed by a Nearest Neigh- bor Classifier in the high-dimensional KPCA feature space. (Carpuat et al., 2004) showed that KPCA- based WSD models achieve close accuracies to the best individual WSD models, while having a signif- icantly different bias.
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Psycholinguistics, Lexicography, and Word Sense Disambiguation

Psycholinguistics, Lexicography, and Word Sense Disambiguation

Hence in the current study, we try to apply the context availability model in our investigation of the relationship between the effectiveness of various knowledge sources (in terms of the disambiguation performance) and the availability of characteristic linguistic context distinguishing one sense from the others for a particular target word. However, we will have to introduce a variation to the model. We have to distinguish between lexical and sense concreteness, the confusion of which is also a major inadequacy in psycholinguistic studies of the concreteness effect. On the one hand, the existence of polysemy means that a word can have multiple senses, but when psycholinguists attempt to norm the concreteness ratings from human subjects, there has been no control on how the subjects actually come up with a rating for the word as a whole. On the other hand, especially in view of the phenomena of sense extensions and metaphorical usages, polysemous words may consist of a mix of both concrete and abstract meanings, and it would make better sense to discuss the concreteness effect at the sense level instead of, or at least in addition to, the word level. This is particularly critical when word sense disambiguation is concerned.
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Word Sense Disambiguation for Machine Translation

Word Sense Disambiguation for Machine Translation

In the remainder of this paper, we first discuss how training data for this task can be acquired au- tomatically from bilingual corpora. We apply a standard learning algorithm for word-sense disam- biguation to the word translation task, with several modifications which proved useful for this task.We present the results of our algorithm on word trans- lation, showing that it significantly improves perfor- mance on this task. We also consider two simple methods for incorporating word translation into ma- chine translation. First, we can use the output of our model to help a translation model choose better words; since general translation is a very noisy pro- cess, we present results on a simplified translation task. Second, we show that the output of our model can be used to prune candidate word sets for trans- lation; this could be used to significantly speed up current translation systems.
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Word sense disambiguation and information retrieval

Word sense disambiguation and information retrieval

However in 1986 Lesk [5] built a disambiguator that used the textual definitions of word senses in an on-line dictionary to provide sense evidence. By using this large reference work, Lesk’s disambiguator had the potential to be applied to large scale problems. The disambiguation technique Lesk used is in fact similar to techniques used in IR. To disambiguate a word w appearing in a certain context (for example, the 20 words surrounding w), the definitions of all the potential senses of w were looked up in the online dictionary. These definitions could be thought of as a small collection of documents. Disambiguation was a ranked retrieval of the definitions using the context as a query. The sense defined by the top ranked definition was chosen as the sense of w.
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