WordNet Synonyms Research Articles

Automated Word Sense Disambiguation Using WordNet Ontology

Automatic word sense disambiguation is a major challenge in natural language processing domain. In recent years, many of supervised and knowledge-based approaches were developed to solve this problem. The use of a sense inventory as a knowledge background to disambiguate words is a very useful technique rather than supervised approaches, with its need of a large pre-trained text corpus. This paper proposes a new approach to disambiguate words in text based on WordNet ontology, as sense inventory. The authors introduce a new technique called Gloss+ for word-sense disambiguation (WSD), which is based on using of the glosses of WordNet synonyms of the target word and the local context in which this word is used. This technique exploits a special behavior of the polysemous synsets. This behavior is detected during the disambiguation process and is used to improve the results obtained. In the experiment part, the authors compare the proposed approach to the methodologies which use synonyms or glosses only.

Question Expansion Technique On The Different Translations Of The Holy Quran By Using WordNet And Islamic Synonyms

The Holy Quran is the most significant religious text, which is followedby thebelievers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language.Most of the Quranic search systems that support theneeds of who are non-native speakers of the Arabic language of the contents of the Holy Quran are basedon keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum,"where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there isno matching between the terms of user's questionandtheterms of the Quran.Consequently, this research has used the synonymsofWordNet to expand the user's question,and to cover all the possibilities of theterms that might be usedin the Quran translation, but there are many of Islamic terms are not available in the WordNet database. The WordNet has been enhanced with a set of Islamic synonyms that have been used in the well-known Islamic translations of the English language, which it was created by the researcher, to increase the likelihood of providing a sure matching between the question's words and the contents of the Quran translation. According to the results of this study, the evaluation of this research proved that the method of question expansion by using the both of the WordNet and the Islamic synonyms showed better accuracy and efficiency than the traditional keywords matching systems, or the system that uses only the WordNet synonyms in the question expansion method in terms of matching all user's questions words with documents and retrieve the correct answers to these questions.

Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish.

BackgroundWhile health literacy is important for people to maintain good health and manage diseases, medical educational texts are often written beyond the reading level of the average individual. To mitigate this disconnect, text simplification research provides methods to increase readability and, therefore, comprehension. One method of text simplification is to isolate particularly difficult terms within a document and replace them with easier synonyms (lexical simplification) or an explanation in plain language (semantic simplification). Unfortunately, existing dictionaries are seldom complete, and consequently, resources for many difficult terms are unavailable. This is the case for English and Spanish resources.ObjectiveOur objective was to automatically generate explanations for difficult terms in both English and Spanish when they are not covered by existing resources. The system we present combines existing resources for explanation generation using a novel algorithm (SubSimplify) to create additional explanations.MethodsSubSimplify uses word-level parsing techniques and specialized medical affix dictionaries to identify the morphological units of a term and then source their definitions. While the underlying resources are different, SubSimplify applies the same principles in both languages. To evaluate our approach, we used term familiarity to identify difficult terms in English and Spanish and then generated explanations for them. For each language, we extracted 400 difficult terms from two different article types (General and Medical topics) balanced for frequency. For English terms, we compared SubSimplify’s explanation with the explanations from the Consumer Health Vocabulary, WordNet Synonyms and Summaries, as well as Word Embedding Vector (WEV) synonyms. For Spanish terms, we compared the explanation to WordNet Summaries and WEV Embedding synonyms. We evaluated quality, coverage, and usefulness for the simplification provided for each term. Quality is the average score from two subject experts on a 1-4 Likert scale (two per language) for the synonyms or explanations provided by the source. Coverage is the number of terms for which a source could provide an explanation. Usefulness is the same expert score, however, with a 0 assigned when no explanations or synonyms were available for a term.ResultsSubSimplify resulted in quality scores of 1.64 for English (P<.001) and 1.49 for Spanish (P<.001), which were lower than those of existing resources (Consumer Health Vocabulary [CHV]=2.81). However, in coverage, SubSimplify outperforms all existing written resources, increasing the coverage from 53.0% to 80.5% in English and from 20.8% to 90.8% in Spanish (P<.001). This result means that the usefulness score of SubSimplify (1.32; P<.001) is greater than that of most existing resources (eg, CHV=0.169).ConclusionsOur approach is intended as an additional resource to existing, manually created resources. It greatly increases the number of difficult terms for which an easier alternative can be made available, resulting in greater actual usefulness.

Generic speech summarization of transcribed lecture videos: Using tags and their semantic relations

We propose a tag‐based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer‐ and author‐assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag‐based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag‐based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.

스피치 요약을 위한 태그의미분석과 잠재의미분석간의 비교 연구

본 연구는 스피치 요약을 위해서 태그를 확장하고 또한 태그 간의 의미적 관계 정보를 이용할 수 있는 태그의미분석 방법을 제안하고 평가하였다. 이를 위해서, 먼저 비디오 태그를 확장하고 태그 간의 의미적 관계를 분석하는데 있어서 플리커의 태그 클러스터와 워드넷의 동의어 정보가 얼마나 효과적으로 이용될 수 있는가 조사해 보았다. 그런 다음 태그의미분석 방법의 특성과 효율성을 조사해 보기 위해서 제안한 방법을 잠재의미분석(Latent Semantic Analysis) 방법과 비교해 보았다. 분석 결과, 플리커의 태그 클러스터는 효과적으로 이용되었지만 워드넷은 효과적으로 이용되지 못한 것으로 나타났다. F측정을 사용하여 두 방법의 효율성을 비교한 결과, 제안한 방법의 F값(0.27)이 잠재의미분석 방법의 F값(0.22)보다 높게 나타났다. We proposed and evaluated a tag semantic analysis method in which original tags are expanded and the semantic relations between original or expanded tags are used to extract key sentences from lecture speech transcripts. To do that, we first investigated how useful Flickr tag clusters and WordNet synonyms are for expanding tags and for detecting the semantic relations between tags. Then, to evaluate our proposed method, we compared it with a latent semantic analysis (LSA) method. As a result, we found that Flick tag clusters are more effective than WordNet synonyms and that the F measure mean (0.27) of the tag semantic analysis method is higher than that of LSA method (0.22).

Topic Classification for Suicidology

Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations. Category: Smart and intelligent computing

Semantic web services discovery based on structural ontology matching

In this paper, we present an approach to semantic-based web service discovery and a prototypical tool based on syntactic and structural schema matching. It is based on matching an input ontology, describing a service request, to web services descriptions at the 'syntactic level' through Web Services Description Language (WSDL) or, at the semantic level, through service ontologies described with languages such as Ontology Web Language for Services (OWL-S), Web Services Modelling Ontology (WSMO), Semantic Web Services Framework (SWSF) and Web Services Description Language Semantics (WSDL-S). The different input schemas, WSDL descriptions, Ontology Web Language (OWL) ontologies, OWL-S, WSMO, SWSF and WSDL-S components are represented in a uniform way by means of directed rooted graphs, where nodes represent schema elements, connected by directed links of different types, e.g., for containment and referential relationships. On this uniform internal representation, a number of matching algorithms operate, including structural-based algorithms (Children Matcher, Leaves Matcher, Graph and SubGraph Isomorphism) and syntactical ones (Edit Distance (Levenshtein Distance or LD) and Synonym Matcher (through the WordNet synonyms thesaurus)).

Using WordNet synonym substitution to enhance UMLS source integration.

Synonym-substitution algorithms have been developed for the purpose of matching source vocabulary terms with existing Unified Medical Language System (UMLS) terms during the integration process. A drawback is the possible explosion in the number of newly generated (potential) synonyms, which can tax computational and expert review resources. Experiments are run using a synonym-substitution approach based on WordNet to see how constraining two methodological parameters, namely, "maximum number of substitutions per term" and "maximum term length," affects performance. Our hypothesis is that these values can be constrained rather tightly--thus greatly speeding up the methodology--without a marked decline in the additional matches produced. Furthermore, we investigate whether a limitation on only the first of the two parameters is sufficient to achieve the same results. A four-stage synonym-substitution methodology using WordNet is presented. A group of experiments is carried out in which the two methodological parameters "maximum number of substitutions per term" and "maximum term length" are varied. The purpose is to examine their effect on the growth in the number of potential synonyms generated and the associated loss of results. The experiments are based on the re-integration of the "Minimal Standard Terminology" (MST) into the UMLS. Synonym-substitution matches found to be inconsistent with the current content of the UMLS and thus deemed to be incorrect are further manually scrutinized as an audit of the original integration of the MST. An increase of 11% in the number of "MST term/UMLS term" matches was achieved using the synonym-substitution methodology. Importantly, this result prevailed when tight threshold values (such as a maximum of two synonym substitutions per term) were imposed on the parameters. Furthermore, it was found that limiting only the "maximum number of substitutions per term" parameter was sufficient to obtain the performance enhancement. During the additional audit phase, a number of the reported mismatches were actually seen to be correct, representing an additional 10% increase in the number of matches obtained. A synonym-substitution methodology that utilizes WordNet is a useful automated aide in UMLS source integration. Experiments showed that there was a significant speed-up but no degradation in match results when the methodology's "maximum number of substitutions per term" parameter was relatively tightly constrained. The methodology also helped to discover errors in the MST's original integration, and improve the quality of the UMLS's conceptual content.