Word Sense Disambiguation Problem Research Articles

Company Name Discrimination in Tweets using Topic Signatures Extracted from News Corpus

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

D-Bees: A novel method inspired by bee colony optimization for solving word sense disambiguation

Word sense disambiguation is an early problem in the field of computational linguistics, and is defined as identifying the sense (or senses) that most likely represents a word, or a sequence of words in a given context. Word sense disambiguation was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximizes the semantic relatedness among the target words. In this paper, we propose a novel algorithm for solving the word sense disambiguation problem, namely D-Bees, that is inspired by the bee colony optimization meta-heuristic in which several artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard SemEval 2007 task 7 coarse-grained English all-words corpus and is compared to the genetic and simulated annealing algorithms as well as an ant colony algorithm. It will follow that the bee and ant colony optimization approaches perform on par achieving better results than the genetic and simulated annealing algorithms on the given dataset.

Automatic Approach for Word Sense Disambiguation Using Genetic Algorithms

Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a collection of documents and produce's a lot of sense to the ambiguities word, the system creates dynamic, and up-todate word sense in a highly automatic method.

Fuzzy Hindi WordNet and Word Sense Disambiguation Using Fuzzy Graph Connectivity Measures

In this article, we propose Fuzzy Hindi WordNet, which is an extended version of Hindi WordNet. The proposed idea of fuzzy relations and their role in modeling Fuzzy Hindi WordNet is explained. We mathematically define fuzzy relations and the composition of these fuzzy relations for this extended version. We show that the concept of composition of fuzzy relations can be used to infer a relation between two words that otherwise are not directly related in Hindi WordNet. Then we propose fuzzy graph connectivity measures that include both local and global measures. These measures are used in determining the significance of a concept (which is represented as a vertex in the fuzzy graph) in a specific context. Finally, we show how these extended measures solve the problem of word sense disambiguation (WSD) effectively, which is useful in many natural language processing applications to improve their performance. Experiments on standard sense tagged corpus for WSD show better results when Fuzzy Hindi WordNet is used in place of Hindi WordNet.

세종 전자사전과 한국어 어휘의미망을 이용한 용언의 어의 중의성 해소

The Sejong Electronic(machine readable) Dictionary, which was developed by the 21 century Sejong Plan, contains a systematic of immanence information of Korean words. It helps in solving the problem of electronical presentation of a general text dictionary commonly used. Word sense disambiguation problems can also be solved using the specific information available in the Sejong Electronic Dictionary. However, the Sejong Electronic Dictionary has a limitation of suggesting structure of sentences and selection-restricted nouns. In this paper, we discuss limitations of word sense disambiguation by using subcategorization information as suggested by the Sejong Electronic Dictionary and generalize selection-restricted noun of argument using Korean Lexico-semantic network.

어절 N-gram을 이용한 문맥의존 철자오류 교정

Context-sensitive spelling-error correction methods are largely classified into rule-based methods and statistical data-based methods, the latter of which is often preferred in research. Statistical error correction methods consider context-sensitive spelling error problems as word-sense disambiguation problems. The method divides a vocabulary pair, for correction, which consists of a correction target vocabulary and a replacement candidate vocabulary, according to the context. The present paper proposes a method that integrates a word-phrase n-gram model into a conventional model in order to improve the performance of the probability model by using a correction vocabulary pair, which was a result of a previous study performed by this research team. The integrated model suggested in this paper includes a method used to interpolate the probability of a sentence calculated through each model and a method used to apply the models, when both methods are sequentially applied. Both aforementioned types of integrated models exhibit relatively high accuracy and reproducibility when compared to conventional models or to a model that uses only an n-gram.

Word sense disambiguation using evolutionary algorithms – Application to Arabic language

Natural language processing is related to human–computer interaction, where several challenges involve natural language understanding. Word sense disambiguation problem consists in the computational assignment of a meaning to a word according to a particular context in which it occurs. Many natural language processing applications, such as machine translation, information retrieval, and information extraction, require this task which occurs at the semantic level. Evolutionary computation approaches can be effective to solve this problem since they have been successfully used for many real-world optimization problems. In this paper, we propose to solve the word sense disambiguation problem using genetic and memetic algorithms, and apply them to Modern Standard Arabic. We demonstrate the performance of several models of our algorithms by carrying out experiments on a large Arabic corpus, and comparing them against a naïve Bayes classifier. Experimental results show that genetic algorithms can achieve more precise prediction than memetic algorithms and naïve Bayes classifier, attaining 79%.

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

AbstractOne of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.

Word Sense Disambiguation Using an Evolutionary Approach

Word sense disambiguation is a combinatorial problem consisting in the computational assignment of a meaning to a word according to a particular context in which it occurs. Many natural language processing applications, such as machine translation, information retrieval, and information extraction, require this task which occurs at the semantic level. Evolutionary computation approaches can be effective to solve this problem since they have been successfully used for many NP-hard optimization problems. In this paper, we investigate main existing methods for the word sense disambiguation problem, propose a genetic algorithm to solve it, and apply it to Modern Standard Arabic. We evaluated its performance on a large corpus and compared it against those of some rival algorithms. The genetic algorithm exhibited more precise prediction results.

SePaS: Word sense disambiguation bysequentialpatterns insentences

AbstractAn open problem in natural language processing is word sense disambiguation (WSD). A word may have several meanings, but WSD is the task of selecting the correct sense of a polysemous word based on its context. Proposed solutions are based on supervised and unsupervised learning methods. The majority of researchers in the area focused on choosing proper size of ‘n’ in n-gram that is used for WSD problem. In this research, the concept has been taken to a new level by using variable ‘n’ and variable size window. The concept is based on the iterative patterns extracted from the text. We show that this type of sequential pattern is more effective than many other solutions for WSD. Using regular data mining algorithms on the extracted features, we significantly outperformed most monolingual WSD solutions. The state-of-the-art results were obtained using external knowledge like various translations of the same sentence. Our method improved the accuracy of the multilingual system more than 4 percent, although we were using monolingual features.

Minimum Description Length Penalization for Group and Multi-Task Sparse Learning

We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an elegant way of incorporating arbitrary sparsity patterns in the feature space by using two-part MDL coding schemes. We present MIC based models for the problems of grouped feature selection (MIC-GROUP) and multi-task feature selection (MIC-MULTI). MIC-GROUP assumes that the features are divided into groups and induces two level sparsity, selecting a subset of the feature groups, and also selecting features within each selected group. MIC-MULTI applies when there are multiple related tasks that share the same set of potentially predictive features. It also induces two level sparsity, selecting a subset of the features, and then selecting which of the tasks each feature should be added to. Lastly, we propose a model, TRANSFEAT, that can be used to transfer knowledge from a set of previously learned tasks to a new task that is expected to share similar features. All three methods are designed for selecting a small set of predictive features from a large pool of candidate features. We demonstrate the effectiveness of our approach with experimental results on data from genomics and from word sense disambiguation problems.

The problem of place name ambiguity

Place name ambiguity is a sub-task of the more general problem of word sense disambiguation. Ide and Véronis [6] concisely define the problem of disambiguation as " matching the context of the instance of the word to be disambiguated with either information from an external knowledge source, or information about the contexts of the word derived from corpora ." Thus, a set of words identified as a place name must be matched to the specific location on the Earth's surface that the author was referring to. The main difference between place name disambiguation and other disambiguation problems is implicit topological and geographic relationships between locations can be exploited for disambiguation.

Research on Word Sense Disambiguation

At present, how to make the computer understand the text message of humanity automatically is a very important issue in computer information technology field. And the problem of word sense disambiguation is a bottleneck of the understanding of natural language. This paper describes the current research situation of word sense disambiguation, introducing its background and application, analyzing various mature approaches, comparing the merits and shortages of those methods, taking a brief introduction of context and international evaluation method of word sense disambiguation and finally making a conclusion of its future and development trend.

Word sense disambiguation methods

Word sense disambiguation is one of the key tasks of text processing. It consists in the determination of senses of words or compound terms in accordance with the context where they were used. The word sense disambiguation problem originated in the 1950s as a subtask of machine translation. Since then, the great number of methods of its solution has been developed; however, none of them may be viewed as a perfect one. The paper is a survey of most well-known studies in this field.

Significance of Novel WSD Algorithms

Word sense disambiguation (WSD) is considered to be a difficult problem in computational linguistics. Single-approach solutions to this problem consisting of only one module are unlikely to yield high performance levels, while hybrid systems formed by combining a number of modules tend to offer a better performance in tackling WSD problems. We propose the strong POS + Frequency baseline as a basic easy-to-implement platform for testing how well algorithms can do when combined with other high-accuracy modules. After giving an overview of the field, we discuss our proposed novel model for WSD, which is a stand-alone contribution in a field in which ideas are repeated. Under the umbrella of our novel WSD model, called Sense Space Model (SSM), we show that significant and interesting algorithms exist. While the accuracy of some of the unsupervised offspring algorithms of the model can be low compared to the strong POS + Frequency baseline (and also compared to the top hybrid systems), sometimes even having an accuracy lower than a random system, such algorithms can still act significantly better than a random system when combined to the strong baseline, considering a meticulous 1% significance level. Therefore, ruling out such lower-accuracy modules from a hybrid system, which might otherwise appear to be a necessary elimination, is challenged. One of these significant algorithms was recently improved by introducing “a threshold” and could beat the implemented POS + Frequency baseline. This confirms that considering such lower-accuracy algorithms as significant is reasonable.

Word Sense Disambiguation and WordNet Technology

This paper discusses the problem of word sense disambiguation in Bangla and its resolution being WordNet like structures. We made AsiaNet, an Machine Readable Dictionary (MRD) and Abhidhan, a thesaurus in Bengali for this purpose and revised the homograph definition. In this work, we aimed for a success rate of 92%.

The bootstrapping of the Yarowsky algorithm in real corpora

The Yarowsky bootstrapping algorithm resolves the homograph-level word sense disambiguation (WSD) problem, which is the sense granularity level required for real natural language processing (NLP) applications. At the same time it resolves the knowledge acquisition bottleneck problem affecting most WSD algorithms and can be easily applied to foreign language corpora. However, this paper shows that the Yarowsky algorithm is significantly less accurate when applied to domain fluctuating, real corpora. This paper also introduces a new bootstrapping methodology that performs much better when applied to these corpora. The accuracy achieved in non-domain fluctuating corpora is not reached due to inherent domain fluctuation ambiguities.

Word sense disambiguation across two domains: Biomedical literature and clinical notes

The aim of this study is to explore the word sense disambiguation (WSD) problem across two biomedical domains—biomedical literature and clinical notes. A supervised machine learning technique was used for the WSD task. One of the challenges addressed is the creation of a suitable clinical corpus with manual sense annotations. This corpus in conjunction with the WSD set from the National Library of Medicine provided the basis for the evaluation of our method across multiple domains and for the comparison of our results to published ones. Noteworthy is that only 20% of the most relevant ambiguous terms within a domain overlap between the two domains, having more senses associated with them in the clinical space than in the biomedical literature space. Experimentation with 28 different feature sets rendered a system achieving an average F-score of 0.82 on the clinical data and 0.86 on the biomedical literature.

SYMCON—A Hybrid Symbolic/Connectionist Systemfor Word Sense Disambiguation

Connectionist methods and knowledge-based techniques are two largely complementary approaches to natural language processing (NLP). However, they both have some potential problems which preclude their being a general purpose processing method. Research reveals that a hybrid processing approach that combines connectionist with symbolic techniques may be able to use the strengths of one processing paradigm to address the weakness of the other one. Hence, a system that effectively combines the two different approaches can be superior to either one in isolation. This paper describes a hybrid system—SYMCON (SYMbolic and CONnectionist) which integrates symbolic and connectionist techniques in an attempt to solve the problem of word sense disambiguation (WSD), which is arguably one of the most fundamental and difficult issues in NLP. It consists of three sub-systems: first, a distributed simple recurrent network (SRN) is trained by using the standard back-propagation algorithm to learn the semantic relationships among concepts, thereby generating categorical constraints that are supplied to the other two sub-systems as the initial results of pre-processing. The second sub-system of SYMCON is a knowledge-based symbolic component consisting of a knowledge base containing general inferencing rules in a certain application domain. Third, a localist network is used to select the best interpretation among multiple alternatives and potentially ambiguous inference paths by spreading activation throughout the network. The structure, initial states, and connection weights of the network are determined by the processing outcome in the other two sub-systems. This localist network can be viewed as a medium between the distributed network and the symbolic sub-system. Such a hybrid symbolic/connectionist system combines information from all three sources to select the most plausible interpretation for ambiguous words.

Cognition, computation, and formal systems: Some of Tomás Havránek's interests and disambiguating word senses

Tomás Havránek's interests were quite wide. This paper argues that his interests in cognitive issues, computer supportable computation methods, and formal systems provide three links to recent work on the old problem of word sense disambiguation. The paper discusses this work on word sense disambiguation, showing both that we have made considerable strides on this difficult problem, but also that our efforts are still feeble compared to our goals of extracting information from texts in natural language.