Top PDF Co-occurrence graphs for word sense disambiguation in the biomedical domain

Co-occurrence graphs for word sense disambiguation in the biomedical domain

Co-occurrence graphs for word sense disambiguation in the biomedical domain

Regardless of whether we refer to general or specific domains, such as the biomedical one, it is commonly accepted in the literature [5, 6, 1] that most WSD algorithms fall into one of the fol- lowing categories: techniques that need labelled training data, and knowledge-based techniques. The first category, also called supervised techniques, usually applies machine learning (ML) al- gorithms to labelled data to develop a model, based on features extracted from the context of the ambiguous words. The development of these features requires a comprehensive understanding of the problem being addressed [7]. We can find many different studies which address general WSD under this supervised point of view, through the use of classical machine learning algorithms [8], and in the last few years also adapting new techniques such as word embeddings [9]. When it comes to the biomedical domain, many works also belong to this category, making use of different ML approaches to address the problem [10, 11, 12, 13], although the bottleneck caused by the scarcity of labelled resources remains a major problem. Other semi-supervised works attempt to relieve this issue by introducing “pseudo-data” to the training examples [14, 15].
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Improving Summarization of Biomedical Documents Using Word Sense Disambiguation

Improving Summarization of Biomedical Documents Using Word Sense Disambiguation

These results demonstrate that employing a state of the art WSD algorithm that has been adapted to use the UMLS Metathesaurus improves the quality of the summaries generated by a sum- marization system. To our knowledge this is the first result to demonstrate that WSD can im- prove summarization systems. However, this im- provement is less than expected and this is prob- ably due to errors made by the WSD system. The Personalized PageRank algorithms (ppr and ppr w2w) have been reported to correctly dis- ambiguate around 58% of words in general text (see Section 2) and, although we were unable to quantify their performance when adapted for the biomedical domain (see Section 5), it is highly likely that they will still make errors. However, the WSD performance they do achieve is good enough to improve the summarization process.
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Evaluating Feature Extraction Methods for Knowledge based Biomedical Word Sense Disambiguation

Evaluating Feature Extraction Methods for Knowledge based Biomedical Word Sense Disambiguation

Surprisingly the results showed that PCA did not obtain a higher accuracy than the explicit co- occurrence vector. We believe this is a result of centering the matrix, and believe that in lan- guage absolute counts are important. When the matrix is centered, only relative counts are con- sidered. This could create a situation where infre- quently used words have distributions similar to commonly used words, adversely effecting results. With respect to dimensionality, we found that low vector dimensionality (d = 100) is sufficient for CBOW and SG, but that a higher dimensional- ity (d = 1500) obtained better results with SVD. In addition, we found that although PCA is com- monly used for dimensionality reduction in many fields, it does not improve results for WSD.
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Can Syntactic and Logical Graphs help Word Sense Disambiguation?

Can Syntactic and Logical Graphs help Word Sense Disambiguation?

This paper presents a word sense disambiguation (WSD) approach based on syntactic and logical representations. The objective here is to run a number of experiments to compare standard contexts (word windows, sentence windows) with contexts provided by a dependency parser (syntactic context) and a logical analyzer (logico-semantic context). The approach presented here relies on a dependency grammar for the syntactic representations. We also use a pattern knowledge base over the syntactic dependencies to extract flat predicative logical representations. These representations (syntactic and logical) are then used to build context vectors that are exploited in the WSD process. Various state-of-the-art algorithms including Simplified Lesk, Banerjee and Pedersen and frequency of co-occurrences are tested with these syntactic and logical contexts. Preliminary results show that defining context vectors based on these features may improve WSD by comparison with classical word and sentence context windows. However, future experiments are needed to provide more evidence over these issues.
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Unsupervised Domain Tuning to Improve Word Sense Disambiguation

Unsupervised Domain Tuning to Improve Word Sense Disambiguation

Using a Wilcoxon signed-rank test, the results were found to be significantly better over the orig- inal algorithms in every case (apart from Topic- Words). Both the WordNet similarity (sim) and the VSM approach (vsm) have a lower performance than the two PPR based WSD algorithms (ppt and w2w). For example, sim assigns the same (usually incorrect) sense to all occurrences of the word tie, while both PPR based algorithms detect an obvious domain change. The vsm approach suffers from a lack of training data (only a small number of exam- ples of each word appear in Semcor), while sim does not get enough information from the context.
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Word Sense Induction & Disambiguation Using Hierarchical Random Graphs

Word Sense Induction & Disambiguation Using Hierarchical Random Graphs

Our future work focuses on using different feature types, e.g. dependency relations, second-order co- occurrences, named entities and others to construct our undirected graphs and then applying HRGs, in order to measure the impact of each feature type on the induced hierarchical structures within a WSD setting. Moreover, following the work in (Clauset et al., 2008), we are also working on using MCMC in order to sample more than one dendrogram at equi- librium, and then combine them to a consensus tree. This consensus tree might be able to express a larger amount of topological features of the initial undi- rected graph.
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Semantic Relatedness for Biomedical Word Sense Disambiguation

Semantic Relatedness for Biomedical Word Sense Disambiguation

In our experiments, the ST profiles were induced from the term-ST co-occurrence matrix. On the other hand, semantic relations and textual definitions in WordNet are useful for word sense disambigua- tion (Ponzetto and Navigli, 2010; Nguyen and Ock, 2011). Hence, the semantic relations between STs and the textual definitions of ST in the Unified Med- ical Language System could be potential resources for the disambiguation of biomedical texts.

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A New Minimally Supervised Framework for Domain Word Sense Disambiguation

A New Minimally Supervised Framework for Domain Word Sense Disambiguation

Domain WSD has been the focus of much interest in the last few years. An important research direc- tion identifies distributionally similar neighbors in raw text as cues for determining the predominant sense of a target word by means of a semantic simi- larity measure (McCarthy et al., 2004; Koeling et al., 2005; McCarthy et al., 2007). Other distributional methods include the use of a word-category cooccur- rence matrix, where categories are coarse senses ob- tained from an existing thesaurus (Mohammad and Hirst, 2006), and synonym-based word occurrence counts (Lapata and Keller, 2007). Domain-informed methods have also been proposed which make use of domain labels as cues for disambiguation purposes (Gliozzo et al., 2004).
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A Review on Word Sense Disambiguation

A Review on Word Sense Disambiguation

ABSTRACT: Word sense disambiguation (WSD) is described as the job of searching the sense of a word in a situation. WSD is a core problem in many tasks related to language processing. It is aggravated by make use of in several critical utilization like Part-of-Speech tagging, Machine Translation, Information retrieval, etc. Different topics such as ambiguity, evaluation, scalability and diversity cause challenges to results of WSD. In this paper we have discussed about some issues related to WSD and some WSD methods like knowledge-based, supervised, unsupervised and semi supervised.
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Subjectivity Word Sense Disambiguation

Subjectivity Word Sense Disambiguation

Things are not as simple in the case of O senses, since they may appear in both subjective and ob- jective expressions. We will state R2, and then ex- plain it: If the contextual classifier labels an in- stance as S, but (1) SWSD determines that it has an O sense, (2) the contextual classifier’s confi- dence is low, and (3) there is no other subjective keyword in the same expression, then R2 flips the contextual classifier’s label to O. First, consider confidence: though a keyword with an O sense may appear in either subjective or objective ex- pressions, it is more likely to appear in an objec- tive expression. We assume that this is reflected to some extent in the contextual classifier’s confi- dence. Second, if a keyword with an O sense ap- pears in a subjective expression, then the subjec- tivity is not due to that keyword but rather due to something else. Thus, the presence of another lex- icon entry “explains away” the presence of the O sense in the subjective expression, and we do not want SWSD to overrule the contextual classifier. Only when the contextual classifier isn’t certain and only when there isn’t another keyword does R2 flip the label to O.
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Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation

Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation

In our task, that is domain adaptations of WSD, we must construct the model of the probability density for each target word. Additionally, the number of in- stances of the target word is too small compared to the number of the feature dimension in both source and target domain. Therefore, an estimated proba- bility density ratio tends to be smaller than the true value, so that some approaches to close the esti- mated probability density ratio to 1 have been pro- posed. Sugiyama translated to the weight w to the weight w p (0 < p < 1) (Sugiyama, 2006), and Ya- mada proposed the relative probability density ratio (Yamada et al., 2011):
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Perceptually Grounded Selectional Preferences

Perceptually Grounded Selectional Preferences

We use the BNC as an approximation of linguis- tic knowledge and a large collection of tagged im- ages and videos from Flickr (www.flickr.com) as an approximation of perceptual knowledge. The human-annotated labels that accompany media on Flickr enable us to acquire predicate-argument co- occurrence information. Our experiments focus on verb preferences for their subjects and direct ob- jects. In summary, our method (1) performs word sense disambiguation and part-of-speech (PoS) tag- ging of Flickr tag sequences to extract verb-noun co-occurrence; (2) clusters nouns to induce SP classes using linguistic and visual features; (3) quantifies the strength of preference of a verb for a given class by interpolating linguistic and visual SP distributions. We investigate the impact of per- ceptual information at different levels – from none (purely text-based model) to 100% (purely visual model). We evaluate our model directly against a dataset of human plausibility judgements of verb- noun pairs, as well as in the context of a semantic task: metaphor interpretation. Our results show that the interpolated model combining linguistic and visual relations outperforms the purely linguis- tic model in both evaluation settings.
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Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora

Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora

Several types of information are useful for WSD (Ide and Veronis 1998). Three major types are the grammatical characteristics of the polysemous word to be disambiguated, words that are syntactically related to the polysemous word, and words that are topically related to the polysemous word. Among these types, use of grammatical characteristics, which are language- dependent, is not compatible with the approach using bilingual corpora. On the other hand, since a topical relation is language-independent, use of topically relat- ed words is most compatible with the approach using bilingual corpora. Accordingly, we focused on using topically related words as clues for determining the most suitable sense of a polysemous word.
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Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation

Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation

The indirect use is to fortify the resource used for WSD. Cai used Bayesian Network for WSD, and improved the original Bayesian Network by innovating the topic features made from topic model to Bayesian Network (Cai et al., 2007). Boyd-Graber introduced the word sense of Word- Net as the additional latent variable into LDA, and used topic model to search synset from WordNet (Boyd-Graber et al., 2007). Li proposed a method of constructing a probability model for WSD de- pending on three circumstances, which Prior prob- ability distribution of word sense was obtained from the corpus or not and the resource of para- phrase in corpus was lacked (Li et al., ).
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder

Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder

classifier using the traing data from source domain; and then, classify the test data from target domain by the classifier (as represented by source tar- get ). There are six domain adaptation patterns: (1) PB OC, (2) OC PB, (3) OC PN, (4) PN OC, (5) PB PN and (6) PN PB. There are six domain adaptations and sixteen target words; the experiments are made 96 ways. We evaluated each methods by following. First, to calculate the accuracy rate for each combination of source do- main, target domain and target word. Then, to cal- culate the average for each domain adaptation. Sim- ilarly, to calculate average of 96 pairs; they are ac- curacy of each methods. In the proposed method, threshold T of similarity is equal to 0.2; if P f > 0.2,
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A comparison of Named Entity Disambiguation and Word Sense Disambiguation

A comparison of Named Entity Disambiguation and Word Sense Disambiguation

could refer to “Robert Redford.” We use the number of links connecting a particular string with a specific entity as a measure of the strength of association. Note that our dictionary spans not just named entities but also many general topics for which there are Wikipedia ar- ticles. Further, it transcends Wikipedia by including an- chors (i) from the greater web; and (ii) to Wikipedia pages that may not (yet) exist. For the purposes of NED, it could make sense to discard all but the articles that correspond to named entities. We keep everything, however, since not all articles have a known entity type, and because we would like to construct a resource that is generally useful for dis- ambiguating concepts. Our dictionary can disambiguate mentions directly, simply by returning the highest-scoring entry for a given string. The construction of this dictionary is explained with more details in (Spitkovsky and Chang, 2012).
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Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Several studies have concentrated specifically on the quality aspect of the MTurk annotations. They investigated methods to assess annotation quality and to aggregate multiple noisy annotations for high reliability. (Snow et al., 2008) report MTurk an- notation quality on various NLP tasks (e.g. WSD, Textual Entailment, Word Similarity) and define a bias correction method for non-expert annota- tors. (Callison-Burch, 2009) uses MTurk workers for manual evaluation of automatic translation qual- ity and experiments with weighed voting to com- bine multiple annotations. (Hsueh et al., 2009) de- fine various annotation quality measures and show that they are useful for selecting annotations leading to more accurate classifiers. Our work investigates the effect of built-in qualifications on the quality of MTurk annotations.
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Word Sense Disambiguation Based on Structured Semantic Space

Word Sense Disambiguation Based on Structured Semantic Space

Word Sense Disambiguation Based on Structured Semantic Space*.. semantic space, as a foundation for word sense.[r]

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On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation

On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation

We wish to thank Diana McCarthy and Rob Koel- ing for kindly providing us the Reuters tagged cor- pora, David Mart´ınez for helping us with the learn- ing features, and Walter Daelemans for his ad- vice on domain adaptation. Oier Lopez de La- calle has a PhD grant from the Basque Govern- ment. This work is partially funded by the Educa- tion Ministry (KNOW TIN2006-15049, OpenMT TIN2006-15307-C03-02) and the Basque Country University (IT-397-07).

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Evaluating a Semantic Network Automatically Constructed from Lexical Co occurrence on a Word Sense Disambiguation Task

Evaluating a Semantic Network Automatically Constructed from Lexical Co occurrence on a Word Sense Disambiguation Task

our target sense nodes rapidly find themselves in- volved with paths to other target sense nodes. This is particularly true of WN++ (notice its rapid and sta- ble convergence), where certain “sticky” nodes form bridges between seemingly unrelated concepts. For example, the frequent appearance of “United States” in Wikipedia articles, and its tendency to be linked to the United States Wikipage when it occurs, causes the term to serve as a bridge between such diverse concepts as automaton#2 and burrito#1, which one would typically expect to be far removed from one another in a model of semantic relatedness.
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