English WordNet Research Articles

English-to-Hindi translation divergence study of English phrasal verbs

English phrasal verbs pose complex semantic interpretations and cause significant problems in their translation into the target language. The paper selects some frequent and highly polysemous English phrasal verbs listed with 10 or more senses in the English WordNet, collects sample English sentences from the British National Corpus and examines their Hindi translation equivalents in terms of the morpho-syntactic structure of Hindi verbs and the representation of semantic information. This study attempts to present the semantic equivalence in rendering the identical semantics in the verbal system of both languages under consideration. The study found that the most frequent and common way of realising the concepts represented by English phrasal verbs in Hindi is by incorporating the compound verb constructions in the Hindi language. Besides that, we observe multiple mapping patterns of English phrasal verbs in Hindi, including the simple Hindi verb (v), compound verb (v1-v2), and conjunct verb (noun/adjective/adverb-verb) constructions.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Abstract Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

Semantic Inheritance in the Lexical Paradigms of Old English Strong Verbs

This work delves into the semantics of Old English lexical paradigms based on strong verbs. Its aim is to describe the patterns of semantic inheritance that hold in these paradigms, which present morphologically related words sharing the form and meaning of the base of derivation. The analysis carried out permits to circumscribe semantic derivation in these Old English lexical paradigms into the lexical entailment relations of troponymy, -troponymy, backward presupposition and cause, and the semantic relations of synonymy and opposition. The data of research has been retrieved from the lexical database Nerthus (Martín Arista et. al 2016). On the theoretical side, this examination follows the English lexical database WordNet (Princeton, 2010). This research unfolds a systematic methodology that is thoroughly described and illustrated by means of the paradigm (ge)berstan. It has not only been possible to circumscribe semantic derivation into the six semantic relations mentioned, but also to determine their frequency of occurrence in the paradigms under analysis, which indicates that synonymy and troponymy are the most recurrent semantic relations.

IDENTIFICATION MODUS OF THE ENGLISH-LANGUAGE ETHICAL CATEGORY GOOD

The article highlights the identification modus of the English-language category GOOD, which is a fundamental constituent of the conceptual construal of the world. The purpose of the study is to establish the categorical and conceptual domains that constitute the identification modus of the category GOOD. The analyzed category is considered as a complete mental construct, which includes three modus: logical, identification and structural. The focus of the study is put on the identification mode, which objectifies its essence and represents how it is viewed in the conceptual sphere and conceptualized by human thinking. The material of the study is the lexicographic definitions of the representative lexemes good, goodness, data from the English language database WordNet 3.1, and contextual fragments from the iWeb English language corpus, obtained by the continuous sampling method, generated by the queries good / goodness is + N; N + is good / goodness. It is emphasized that the category is rigidly structured by the idealized cognitive models that represent its prototype and are expressed at the linguistic level by the semantics of its verbalizers. Such models act as a way of organizing knowledge about an object or phenomenon of the surrounding reality and are represented by the underlying prototype. The conducted analysis made it possible to identify three such models “moral soundness”, “usefulness & desirability” and “commodity”, each of which is discussed separately. It is noted that the idealized cognitive model is represented by two aspects: classification and characterization. The classification aspect represents a classification grid, in the nodes of which there are domains that act as superordinate concepts of the studied category. The characteristic aspect represents the semantic filling of the grid with features that form a categorical semantic prototype. The features of the categorical prototype act as a certain reference frame when categorizing objects and phenomena of the objective reality in relation to domains of a more general order, which constitute the classification aspect of each of the idealized cognitive models. The results of the study prove that the conceptualization of the abstract idea of good is based on knowledge about material physical goods.

Applying Similarity Measures to Improve Query Expansion

The huge evolving in the information technologies, especially in the few last decades, has produced an increase in the volume of data on the World Wide Web, which is still growing significantly. Retrieving the relevant information on the Internet or any data source with a query created by a few words has become a big challenge. To override this, query expansion (QE) has an important function in improving the information retrieval (IR), where the original query of user is recreated to a new query by appending new related terms with the same importance. One of the problems of query expansion is the choosing of suitable terms. This problem leads to another challenge of how to retrieve the important documents with high precision, high recall, and high F measure. In this paper, we solve this problem through applying different similarity measures with the use of English WordNet. The obtained results proved that, with a suitable selection method, we are able to take advantage of English WordNet to improve the retrieval efficiency. The work proposed in this paper is extracting the terms from all the documents and query, then applying the following steps: preprocessing, expanding the query based on English WordNet, selecting the best terms, weighting of term, and finally using the cosine similarity and Jaccard similarity to obtain the relevant documents.
 Our practical results were applied on the DUC2002 dataset that contains 559 documents distributed over several categories. The average precision of cosine (for random queries) = 100% whereas the average precision of Jaccard = 84.4 %, and the average recall of cosine = 86.8% whereas the average recall of Jaccard = 73.4%. The average f-measure of cosine = 92%, whereas the average f-measure of Jaccard = 76%.

A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNet

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question answering, sentiment analysis, and emotion recognition. EWN includes sets of cognitive synonyms called synsets, which are interlinked by means of conceptual-semantic and lexical relations and where each synset expresses a distinct concept. However, other languages are still lagging behind in having large-scale and rich lexical resources similar to EWN. In this article, we focus on enabling the development of such resources for Arabic. While there have been efforts in developing an Arabic WordNet (AWN), the current version of AWN has its limitations in size and in lacking transliteration standards, which are important for compatibility with Arabic NLP tools. Previous efforts for extending AWN resulted in a lexicon, called ArSenL, that overcame the size and the transliteration standard limitation but was limited in accuracy due to the heuristic approach that only considered surface matching between the English definitions from the Standard Arabic Morphological Analyzer (SAMA) and EWN synset terms, and that resulted in inaccurate mapping of Arabic lemmas to EWN’s synsets. Furthermore, there has been limited exploration of other expansion methods due to expensive manual validation needed. To address these limitations of simultaneously having large-scale size with high accuracy and standard representations, the mapping problem is formulated as a link prediction problem between a large-scale Arabic lexicon and EWN, where a word in one lexicon is linked to a word in another lexicon if the two words are semantically related. We use a semi-supervised approach to create a training dataset by finding common terms in the large-scale Arabic resource and AWN. This set of data becomes implicitly linked to EWN and can be used for training and evaluating prediction models. We propose the use of a two-step Boosting method, where the first step aims at linking English translations of SAMA’s terms to EWN’s synsets. The second step uses surface similarity between SAMA’s glosses and EWN’s synsets. The method results in a new large-scale Arabic lexicon that we call ArSenL 2.0 as a sequel to the previously developed sentiment lexicon ArSenL. A comprehensive study covering both intrinsic and extrinsic evaluations shows the superiority of the method compared to several baseline and state-of-the-art link prediction methods. Compared to previously developed ArSenL, ArSenL 2.0 included a larger set of sentimentally charged adjectives and verbs. It also showed higher linking accuracy on the ground truth data compared to previous ArSenL. For extrinsic evaluation, ArSenL 2.0 was used for sentiment analysis and showed, here, too, higher accuracy compared to previous ArSenL.

WordNet construction for under-resourced languages using personalized PageRank

Abstract Wordnet development is an active research area among NLP researchers. Since the manual construction of the English wordnet was very costly both in terms of time and human expertise, automatic approaches have become very popular for wordnet development in languages other than English. Automatic methods usually benefit from an existing wordnet of a high resource language and use it as the backbone of their work. In this article, we present an unsupervised approach for automatic wordnet construction using a combination of Expectation–Maximization and personalized PageRank algorithms. Our method uses some typical and available language resources, so it is applicable to many languages including under-resourced ones. The proposed method needs just a bilingual dictionary and a monolingual corpus for developing a wordnet. In order to evaluate the proposed method, we apply it to the Persian language which is identified as an under-resourced language in NLP tasks. Evaluation results properly indicate the power of the proposed method to construct a high quality and large-scale wordnet for poor-resource languages. According to experiments, we achieve a precision of higher than 93% with a recall of 50%.

Building Synonym Sets for English WordNet with Robust Clustering using Links Method

English WordNet is an important synonym set to present the similarity of meanings between words. Synonym Set is built using Oxford Thesaurus which is accessed through lexico.com, which is a part of the lexical database that will be used. After using the extraction process through Oxford Thesaurus it will produce a synonym set with the same meaning between words. The difference between WordNet and ordinary dictionaries is that the word is interconnected with other words. One method employed for this approach is Robust Clustering Using Links method, which is similarity values and synonym sets that have been created to be used to build a lexical database. Therefore the main purpose of the development of the English WordNet is to produce an accurate synonym set using clustering techniques. The evaluation calculation will use the F-measure method and will use the gold standard for the calculation method. With the ROCK method, there is an increase in accuracy output from dataset input. Building the English wordnet is to improve words that can be used to help research and development of other language wordnets with role models using more accurate English wordnets. And the use of ROCK method there is an increase in the accuracy upon results of the development of English wordnet compared to the previous method, which is using hierarchical clustering. The outcome of this study resulted in improved accuracy so that the ROCK method is one of the good methods used in the development of the English wordnet.

Development Synonym Set for the English Wordnet Using the Method of Comutative and Agglomerative Clustering

Wordnet is a collection of words that interpret or present a meaning, in its development Wordnet has an important part, the Synonym Set or Synset. In making Synonym sets, synonyms are needed and the commutative nature of words is needed. To get word synonyms, the English language thesaurus becomes the reference data for taking synonym data. Broadly speaking, the difference between Wordnet and the dictionary is that the meaning of the word is related to other words, to determine the equation requires a commutative process. The process is made easy by using commutative methods that will produce a candidate synonym set. Candidates for the synonym set cannot be used for word syntax, the grouping process of words which produces the Synonym set as the final result must be carried out. The process of grouping words can one of them use clustering techniques, in this study will use Agglomerative Clustering techniques. In the process of agglomerative clustering techniques there is a threshold value to determine the number of repetitions or as a condition to stop the iteration process. The clustering process in this study will use a threshold value of 0.1 to 1 to test the best threshold value to produce the best Synonym set and calculate its accuracy value. Accuracy calculation and evaluation will use the F-measure method to find the best results.

Knowledge-Based Semantic Relatedness measure using Semantic features

Measuring semantic relatedness has received much attention for uses in many fields such as information retrieval and natural language processing. For handling synonymous problem in distributional-based measures, many researchers are investigating how to exploit semantic features in lexical sources to form knowledge-based measures. In the knowledge-based measures, a hierarchy model is used to measure the relatedness between words based on only the taxonomical features extracted from a provided lexical source. In this paper, a new knowledge feature-based measure is proposed to build the semantic vector of a word construct on taxonomical and non-taxonomical feature of relation words. The proposed measure utilised the topological parameters that weight the importance of each element in the semantic vector. One of the gold dataset used to assess the proposed model and compare the findings with other related works. The results demonstrated the effectiveness of the proposed model on measuring semantic relatedness between words. In this paper, the research framework is identified based on the observations made on the previous related works that have been conducted for semantic representation and semantic relatedness measures. The required data in this research includes the semantic knowledge-based approach and the evaluation datasets. The semantic knowledge that will be used throughout of this research is extracted from English WordNet 3.1. On the other hand, the evaluation datasets covers the gold standard benchmarks which have been used for evaluating the semantic relatedness measurements and text mining tasks. Finally, the evaluation is preform to evaluate the proposed method (PM) based on approach in this research, in which obtained the result have been analyzed, to discuss and compare based on different performance measure and finding the strength and weakness in this paper, to alternative the semantic representation correlated to this research, to designing and develop the topical-based on the semantic representation method for text mining from Social media.

Word Sense Disambiguation in Tamil using Indo Wordnet and Cross-Language Semantic Similarity

Word sense disambiguation is the way to compute the correct sense of a word. It is considered as one of the important subtasks in natural language processing, machine translation and information retrieval. WSD found improving the overall performances of these systems. The job of WSD is to eliminate all senses of a word except the appropriate one as per the given context. The work in Tamil linguistics domain for information retrieval or natural language processing is very less. WSD can be performed in supervised and unsupervised manner. Here, we have proposed an unsupervised approach to disambiguate Tamil words in a given context using the context words and their dictionary gloss definitions. We have proposed two variants of our approach. The first approach uses the number of word overlapping between the glosses of context words whereas the second one uses the similarity between the glosses of context words with that of the ambiguous word. The second one found best among the two. For our approach, we have used Tamil Indo-WordNet, Oxford Tamil Dictionary and English WordNet dictionary glosses. Our method achieves better result in recognising correct senses in Tamil text.

Improving Semantic Similarity with Cross-Lingual Resources: A Study in Bangla—A Low Resourced Language

Semantic similarity is a long-standing problem in natural language processing (NLP). It is a topic of great interest as its understanding can provide a look into how human beings comprehend meaning and make associations between words. However, when this problem is looked at from the viewpoint of machine understanding, particularly for under resourced languages, it poses a different problem altogether. In this paper, semantic similarity is explored in Bangla, a less resourced language. For ameliorating the situation in such languages, the most rudimentary method (path-based) and the latest state-of-the-art method (Word2Vec) for semantic similarity calculation were augmented using cross-lingual resources in English and the results obtained are truly astonishing. In the presented paper, two semantic similarity approaches have been explored in Bangla, namely the path-based and distributional model and their cross-lingual counterparts were synthesized in light of the English WordNet and Corpora. The proposed methods were evaluated on a dataset comprising of 162 Bangla word pairs, which were annotated by five expert raters. The correlation scores obtained between the four metrics and human evaluation scores demonstrate a marked enhancement that the cross-lingual approach brings into the process of semantic similarity calculation for Bangla.

Semantically Enhanced Concept Search of the Holy Quran: Qur’anic English WordNet

This paper presents a framework to perform concept- and keyword-based English search of the Holy Quran. The concept-based search is challenging as it should be able to handle the abstract queries which often do not occur verbatim in text. In order to implement it, a Qur’anic English WordNet is implemented, which is a WordNet-derived database for the English translations of the Qur’an to form the basis of a semantically rich resource for Qur’an search system. WordNet structure of semantically linked information is used as baseline and is augmented with a few newly created relations to capture Qur’an specific information. A Vocabulary of Qur’anic Concepts is also automatically extracted from the English Qur’anic translations and is made available in the form of conceptual hierarchy. Finally, an information retrieval tool, Qur’an enhanced search tool, is developed to perform semantically enhanced search based on the newly created knowledge resources. The strength of the proposed scheme is demonstrated by comparing the information retrieved against other similar tools, and results are found to be encouraging with best performance in terms of recall. Furthermore, the semiautomatic approach of creating the knowledge repository used in this research is scalable as compared to other existing systems which are manually created. The proposed system is easily extendable for other religious knowledge texts such as Islamic Jurisprudence and Hadeeth.

Similarity Detection Using Latent Semantic Analysis Algorithm

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

Efficient Use of Resources for Statistical Machine Translation

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Machine translation has great potential to expand the audience for ever increasing digital collections. Success of data driven machine translation systems is governed by the volume of parallel data on which these systems are being modelled. The languages which do not have such resources in huge quantity, the optimum utilisation of them can only be assured through their quality. Morphologically rich language like Hindi poses further challenge, due to </span><span>having more number of orthographic inflections for a given word and presence of non-standard word spellings in </span><span>the corpus. This increases the chances of getting more number of words which are unseen in the training corpus. In this paper, the objective is to reduce redundancy of available corpus and utilise the other resources as well, to make best use of resources. Reduction in number of words unseen to the translation model is achieved through text noise removal, spell normalisation and utilising English WordNet (EWN). The test case presented here is for English-Hindi language pair. The results achieved are promising and set example for other morphological rich languages to optimise the resources to improve the performance of the translation system. </span></p></div></div></div>

Efficient Use of Resources for Statistical Machine Translation

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Machine translation has great potential to expand the audience for ever increasing digital collections. Success of data driven machine translation systems is governed by the volume of parallel data on which these systems are being modelled. The languages which do not have such resources in huge quantity, the optimum utilisation of them can only be assured through their quality. Morphologically rich language like Hindi poses further challenge, due to </span><span>having more number of orthographic inflections for a given word and presence of non-standard word spellings in </span><span>the corpus. This increases the chances of getting more number of words which are unseen in the training corpus. In this paper, the objective is to reduce redundancy of available corpus and utilise the other resources as well, to make best use of resources. Reduction in number of words unseen to the translation model is achieved through text noise removal, spell normalisation and utilising English WordNet (EWN). The test case presented here is for English-Hindi language pair. The results achieved are promising and set example for other morphological rich languages to optimise the resources to improve the performance of the translation system. </span></p></div></div></div>

GENERATING A MALAY SENTIMENT LEXICON BASED ON WORDNET

Sentiment lexicon is a list of vocabularies that consists of positive and negative words. In opinion mining, sentiment lexicon is one of the important source in text polarity classification task in sentiment analysis model. Studies in Malay sentiment analysis is increasing since the volume of sentiment data is growing on social media. Therefore, requirement in Malay sentiment lexicon is high. However, Malay sentiment lexicon development is a difficult task due to the scarcity of Malay language resource. Thus, various approaches and techniques are used to generate sentiment lexicon. The objective of this paper is to develop Malay sentiment lexicon generation algorithm based on WordNet. In this study, the method is to map the WordNet Bahasa with English WordNet to get the offset value of a seed set of sentiment words. The seed set is used to generate the synonym and antonym semantic relation in English WordNet. The highest result achives 86.58% agreement with human annotators and 91.31% F1-measure in word polarity classification. The result shows the effectiveness of the proposed algorithm to generate Malay sentiment lexicon based on WordNet.

An Optimized Lesk-Based Algorithm for Word Sense Disambiguation

Abstract Computational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents based on the theory underlying topic models. The knowledge resource adopted is the English WordNet enriched with linguistic knowledge from Wikipedia and Semcor corpus. Besides the algorithm’s eficiency, we also evaluate its efectiveness using two datasets; a general domain dataset and domain-specific dataset. The algorithm achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.

Automatically Generating A Sentiment Lexicon For The Malay Language

This paper aims to propose an automated sentiment lexicon generation model specifically designed for the Malay language. Lexicon-based Sentiment Analysis (SA) models make use of a sentiment lexicon for SA tasks, which is a linguistic resource that comprises a priori information about the sentiment properties of words. A sentiment lexicon is an indispensable resource for SA tasks. This is evident in the emergence of a large volume of research focused on the development of sentiment lexicon generation algorithms. This is not the case for low-resource languages such as Malay, for which there is a lack of research focused on this particular area. This has brought up the motivation to propose a sentiment lexicon generation algorithm for this language. WordNet Bahasa was first mapped onto the English WordNet to construct a multilingual word network. A seed set of prototypical positive and negative terms was then automatically expanded by recursively adding terms linked via WordNet’s synonymy and antonymy semantic relations. The underlying intuition is that the sentiment properties of newly added terms via these relations are preserved. A supervised classifier was employed for the word-polarity tagging task, with textual representations of the expanded seed set as features. Evaluation of the model against the General Inquirer lexicon as a benchmark demonstrates that it performs with reasonable accuracy. This paper aims to provide a foundation for further research for the Malay language in this area.

Gujarati WordNet A Lexical Database

The English WordNet and the Hindi WordNet have inspired the Gujarati WordNet; this paper specifies the overview and the basic methods for developing a Gujarati WordNet. General Terms WordNet lies under the category of Natural Language Processing the result shows all the possible meanings, it depends on how humans think and how the language is exactly used.