A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach W3SD (This paper is an extended version of our work [4] published in the 8th International Conference on Computational Collective Intelligence.) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using WordNet and Wiktionary features through the taxonomic information content from WordNet and the glosses from Wiktionary. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks.

Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human’s natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document’s main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms’ order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.

This work proposes a novel distributed scheme based on a part-of-speech tagged lexicographic encoding to represent the context in which a particular word occurs in an evolutionary approach for word sense disambiguation. Tagged dataset for every sense of a polysemous word are considered as inputs to supervised classifiers, Artificial Neural Networks (ANNs), which are evolved by a joint optimization of their structures and weights, together with a similarity based recombination operator.The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entries of the Semeval-2007 competition has shown that the proposed approach is competitive with state-of-the-art WSD approaches.KeywordsWord Sense DisambiguationComputational LinguisticsSemantic EvaluationPolysemous WordWord InstanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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This paper presents an approach for word sense disambiguation (WSD) of image tags from professional and social image databases without categorial labels, using WordNet as an external resource defining word senses. We focus on the resolution of lexical ambiguity that arises when a given keyword has several different meanings. Our approach combines some knowledge-based methods (Lesk algorithm and Hyponym Heuristics) and semantic measures, in order to achieve successful disambiguation. Experimental results and performance evaluation are 95.16 % accuracy for professional images and 87.40 % accuracy for social images, for keywords included in WordNet. This approach can be used for improving machine translation of tags or image similarity measurement.

Natural Languages are inherently ambiguous. Word Sense Disambiguation is one such problem where one word has multiple meaning depending upon the context in which it appears in the text. It can be considered as an intermediate step for many NLP applications like Machine Translation, Summarization, Query Processing, etc. Developing a Word Sense Disambiguation (WSD) system for the Gujarati language can have significant applications in the education sector. There are different approaches available for WSD which includes Supervised, Unsupervised and knowledge-based approaches. The work done is English language is extensive for this problem but for other regional languages more research needs to be done. This paper presents various works, mentioning their proposed method, datasets used, limitations and performance for some Indian languages like Hindi, Malayalam, Bengali, etc. The paper also enlists some general observations about existing approaches for word sense disambiguation. In the last section, the paper proposes a method to resolve WSD problem for Gujarati Language using Genetic Algorithm.

Word Sense Disambiguation (WSD) is the process of resolving the meaning of a word unambiguously in a given natural language context. Within the scope of this thesis, it is the process of marking text with explicit sense labels. What constitutes a sense is a subject of great debate. An appealing perspective, aims to define senses in terms of their multilingual correspondences, an idea explored by several researchers, Dyvik (1998), Ide (1999), Resnik & Yarowsky (1999), and Chugur, Gonzalo & Verdejo (2002) but to date it has not been given any practical demonstration. This thesis is an empirical validation of these ideas of characterizing word meaning using cross-linguistic correspondences. The idea is that word meaning or word sense is quantifiable as much as it is uniquely translated in some language or set of languages. Consequently, we address the problem of WSD from a multilingual perspective; we expand the notion of context to encompass multilingual evidence. We devise a new approach to resolve word sense ambiguity in natural language, using a source of information that was never exploited on a large scale for WSD before. The core of the work presented builds on exploiting word correspondences across languages for sense distinction. In essence, it is a practical and functional implementation of a basic idea common to research interest in defining word meanings in cross-linguistic terms. We devise an algorithm, SALAAM for Sense Assignment Leveraging Alignment And Multilinguality, that empirically investigates the feasibility and the validity of utilizing translations for WSD. SALAAM is an unsupervised approach for word sense tagging of large amounts of text given a parallel corpus—texts in translation—and a sense inventory for one of the languages in the corpus. Using SALAAM, we obtain large amounts of sense annotated data in both languages of the parallel corpus, simultaneously. The quality of the tagging is rigorously evaluated for both languages of the corpora. The automatic unsupervised tagged data produced by SALAAM is further utilized to bootstrap a supervised learning WSD system, in essence, combining supervised and unsupervised approaches in an intelligent way to alleviate the resources acquisition bottleneck for supervised methods. Essentially, SALAAM is extended as an unsupervised approach for WSD within a learning framework; in many of the cases of the words disambiguated, SALAAM coupled with the machine learning system rivals the performance of a canonical supervised WSD system that relies on human tagged data for training. Realizing the fundamental role of similarity for SALAAM, we investigate different dimensions of semantic similarity as it applies to verbs since they are relatively more complex than nouns, which are the focus of the previous evaluations. We design a human judgment experiment to obtain human ratings on verbs' semantic similarity. The obtained human ratings are cast as a reference point for comparing different automated similarity measures that crucially rely on various sources of information. Finally, a cognitively salient model integrating human judgments in SALAAM is proposed as a means of improving its performance on sense disambiguation for verbs in particular and other word types in general.

The identification of the correct sense of a word is necessary for many tasks in automatic natural language processing like machine translation, information retrieval, speech and text processing. Automatic Word Sense Disambiguation (WSD) is difficult and accuracies with state-of-the art methods are substantially lower than in other areas of text understanding like part-of-speech tagging. One shortcoming of these methods is that they do not utilize substantial sources of background knowledge, such as semantic taxonomies and dictionaries, which are now available in electronic form (the methods largely use shallow syntactic features). Empirical results from the use of Inductive Logic Programming (ILP) have repeatedly shown the ability of ILP systems to use diverse sources of background knowledge. In this paper we investigate the use of ILP for WSD in two different ways: (a) as a stand-alone constructor of models for WSD; and (b) to build interesting features, which can then be used by standard model-builders such as SVM. In our experiments we examine a monolingual WSD task using the 32 English verbs contained in the SENSEVAL-3 benchmark data; and a bilingual WSD task using 7 highly ambiguous verbs in machine translation from English to Portuguese. Background knowledge available is from eight sources that provide a wide range of syntactic and semantic information. For both WSD tasks, experimental results show that ILP-constructed models and models built using ILP-generated features have higher accuracies than those obtained using a state-of-the art feature-based technique equipped with shallow syntactic features. This suggests that the use of ILP with diverse sources of background knowledge can provide one way for making substantial progress in the field of automatic WSD.

Word-sense disambiguation is an open challenge in natural language processing. It is the process of identifying the actual meaning of the word based on the senses of the surrounding words of the context in which it is used. Knowledge-based approaches are becoming most popular than other approaches for word-sense disambiguation. Knowledge-based approaches do not require large volumes of training data instead uses Lexical knowledge bases to construct undirected graphs. In this paper, traditional Page Rank algorithms and random walk approaches are compared extensively.

At present, the focus of ontology learning is on the extraction of concepts and relations. The relation extraction is divided into hierarchical relation extraction and non-hierarchical relation extraction, and hierarchical relation extraction is the basis of non-hierarchical relation extraction. In this paper, the method of mixed hierarchical clustering is used to extract and classify domain concepts from textual corpora in different fields of Uyghur language. Due to the limitations of the text content selected in this paper and the difficulty of merging nodes by hierarchical clustering, although the results are not ideal, they basically conform to the ontology hierarchy form. The experimental results show that the method is feasible, and the factors such as increasing the vector dimension and the text content, can further improve the accuracy of the domain concept and relation.

Word sense disambiguation (WSD) refers to the task of finding the intended meaning of a word in a given sentence. WordNet® (lexical database based on psychological principals) based on WSD methods have been discussed extensively. WordNet® relates words through various relations and all the WSD approaches available in the literature till date rely on the notion of assigning equal importance to each relation. But actually in real-world situations, these relations do not exhibit equal importance, so they should not be given equal weight. The paper presents a novel method for exploiting the idea of WordNet® relations weight based on its importance and thus assigns the weights to the edges of the WordNet® graph, µ ∊ (0, 1]. This helps in fuzzifying the WordNet® graph, where nodes represent the words and weighted edges represent the relation of the words with strength∈ (0,1]. The values for edge weight assignment are obtained using test cases based on a simulated annealing method. The proposed method utilizes various Fuzzy graph connectivity measures to determine the significance of each node in the Fuzzy graph, resulting in identification of the intended meaning of the word. This method is tested on the SemCor dataset and observed that it gives better results when compared to state-of-the-art approaches.

Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.

Word Sense Disambiguation (WSD) is the procedure of selecting precise sense or meaning for a word in a given context. Word sense disambiguation goes about as an establishment to different AI applications as data mining, information recovery, and machine interpretation. The issue solicits to figure out which sense from the polysemous word is appropriate in a given context. Previously a few methodologies have been proposed for WSD in English, German and so forth, however, work on WSD in Hindi is limited. In this work, a genetic algorithm based approach is presented for Hindi WSD. The feature of dynamic setting window is used which contains left and right expression of a vague word. The cardinal supposition of this approach is that the target word must have a common topic in its neighborhood. For finding all possible senses of an ambiguous word, WordNet created by IIT Bombay is used.

Microblogging is used widely to express opinions toward an entity, knowing sentiment polarity can get benefit for decision making, planning, and visualization and so on. Outdated training data along with the nature of Microblogging which is short and noisy cause low accuracy. Existing approach requires human effort to manually label huge training data. To tackle these problems, we proposed a framework that used a hybrid approach between lexicon-based approach and machine learning approach. SentiWordnet has been used to automatically label training data and then using Support Vector Machine for sentiment classification. We study two scoring mechanisms for labeling training data, Word Sense Disambiguation and Non Word Sense Disambiguation. The framework also used MapReduce for computing large dataset. The result shows that Non Word Sense Disambiguation is optimal for this framework. The framework is functional, more automatically and less human efforts.

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as text-to-onto, has shown that CRCTOL produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures.