Degree Of Semantic Similarity Research Articles

Measuring the semantic headedness of English blends with token-based semantic vector space modeling: a corpus-based study

Abstract This article analyzes the semantic headedness of English blends with distributional semantics methods. The semantic head of a blend is the source word that transfers its semantic information to the blend as a whole. For example, a sitcom is a kind of comedy. But is FedEx a kind of express, and is wi-fi a kind of fidelity? We use corpus data and token-based semantic vector space modeling in order to address these questions. Specifically, we investigate whether Plag’s ternary division of endocentric, exocentric, and coordinative compounds based on semantic headedness can also be applied to English blends, and whether the general tendency of semantic right-headedness can be observed for all three subtypes. We analyze a dataset of fifty-five blends and their respective source words, using data from the Corpus of Contemporary American English and the English Web Corpus 2021. We measure the degree of semantic similarity between each blend and its two source words. The results show that for most endocentric blends, the hypothesis of semantic right-headedness holds true. At the same time, exocentric blends and coordinative blends are shown to behave differently. We conclude that Plag’s classification offers a useful point of departure for the semantic analysis of blends and that distributional semantics methods can provide new insights into their semantic behavior.

The family size effect in visual and auditory word recognition

ABSTRACT Words with larger morphological families elicit shorter response times in visual lexical decision, but in auditory lexical decision, family size effects have been reported to be facilitative, absent, or inhibitory. We revisit the auditory family size effect and different family size definitions by re-analysing data from two large lexical decision experiments. The results suggest that words with larger families elicit smaller response latencies not only in visual but also in auditory lexical decision. However, in auditory lexical decision, this effect is insignificant when the word contains a prefix. In both modalities, it is the degree of semantic similarity and the degree of form overlap between a word and its family members, rather than the mere presence of overlap, that seem to drive the family size effect.

Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models

Abstract In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.

Automated lexical analysis of story recall in healthy controls

AbstractBackgroundImpaired episodic memory is one of the earliest and most prominent symptoms of Alzheimer’s disease (AD). Examining how patients produce words and phrases in story recall tasks is useful for tracking and understanding progression of their disease. Traditional approaches rely on manual assessments of recalled stories which can be limited, time‐consuming, and not applicable in large‐scale studies. Here, we implement an automated, computational approach to measure verbal episodic memory in a digitally recorded story recall task performed by young healthy speakers using natural language processing models.MethodWe analyzed digitized speech samples of pre‐recorded immediate and delayed Craft Story recall tasks performed by healthy speakers (n = 67, mean age = 20.31 years, SD = 2.17, 45 (67%) females). All transcripts were transcribed by trained annotators and automatically scored for number of verbatim and paraphrase recall, total recall score ( = verbatim+paraphrase), and semantic distance from the original story (degree of semantic similarity between the original craft story and the recalled story based on the Word2vec module in python). We also calculated total word count and number of unique words, and rated all recalled words for word familiarity, concreteness, and semantic ambiguity based on published norms. Number of pauses and total durations of speech and silent pauses were also measured.ResultWe found larger semantic distance between successive words in the delayed recall task compared with immediate recall within speaker (p<.001). Delayed recall also elicited lower total recall scores than immediate recall (p = .03). Comparing the effect sizes of semantic distance vs. total recall scores, our model comparison showed a smaller prediction error for semantic distance (AIC = 95.45) than total recall score (AIC = 736.18). In addition, speakers in the delayed recall task performed more paraphrase recall, had higher total word count, number of unique words, more ambiguous words, and produced more speech (all p‐values<.001).ConclusionOur findings suggest that automated speech analysis can provide informative and cost‐effective measures from a recorded story recall task. The study provides an automated and quantitative way to better measure memory abilities by including semantic distance. Future work will test procedures in patients with AD or other neurodegenerative diseases.

Sentiment Analysis of Social Network Text Based on HDBSCAN and SO-PMI

With the rapid development of the Internet, the length and themes of text content in social network environments are becoming more and more abundant. The probability and degree of semantic similarity between different words in different texts are also increasing. It is difficult to effectively identify text topics and their sentiment tendencies. This paper takes the text data of the Ford Forum of the PCauto as the research object, builds a word vector model based on Word2vec, uses HDBSCAN to classify social network context, and calculates the sentiment value of different topic through SO-PMI. It aims to explore the complexity of the semantic evolution and feature extraction process and its generating text topics, based on which we further analyze the variability of sentiment tendencies between words and topics and then summarize their characteristics.

Neurofuzzy semantic similarity measurement

Automatically identifying the degree of semantic similarity between two small pieces of text has grown in importance recently. Its impact on various computer-related domains and recent breakthroughs in neural computation has increased the opportunities for better solutions to be developed. This work contributes a neurofuzzy approach for semantic textual similarity that uses neural networks and fuzzy logics. The idea is to combine the capabilities of the deep neural models for working with text with the ones from fuzzy logic for aggregating numerical data. The results of our experiments suggest that such an approach can accurately determine semantic similarity.

Formation Networks of Terms for Identifying Semantic Similarity or Difference Degree of Texts in Cybersecurity


 
 
 This paper devoted the problem of identifying a semantic similarity degree or difference of text in cybersecurity field. The paper presents a method for comparing text documents based on the formation and comparison of the corresponding semantic networks. The directed weighted network of terms, where the nodes of such networks are key terms of the text, and edges are semantic relationships between these terms in the text are considered as a semantic network. The algorithm for formation semantic networks as one of the types of ontologies is also presented. Formation of network of term includes pre-processing of text data, extraction of key terms, construction of undirected network of terms (using the algorithm of horizontal visibility graph), determining undirected connections between terms, and further determining the directions of connections and their weight values. The Frobenius norm of the difference of matrices corresponding to the semantic networks is considered to compare the semantic networks. An identifying the critically different texts that can have similar keywords but different semantic between them is important to ensure cybersecurity. Also, the proposed approach can be helpful while solving the problem of accumulating text data semantically similar in content. In general, this approach can also be used in systems of automatic information retrieval to determine the degree of similarity or difference in the structure and semantics of texts and identify the sources of information that have a destructive impact on the information space.
 
 

Assessing naming errors using an automated machine learning approach.

After left hemisphere stroke, 20%-50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing "violin" for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower interrater reliability because of subjectivity. We investigated whether a computational linguistic measure using word2vec (Mikolov, Chen, et al., 2013) addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N = 105). Pearson correlations demonstrated excellent convergent validity of word2vec's semantically related estimates of naming errors and independent tests of access to lexical-semantic knowledge (p < .0001). Further, multiple regression analysis showed word2vec's semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical-semantic knowledge. Useful to both theorists and clinicians, our word2vec-based method provides an automated, continuous, and objective psychometric measure of access to lexical-semantic knowledge during naming. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Technology opportunity discovery based on patent analysis: a hybrid approach of subject-action-object and generative topographic mapping

ABSTRACT An incomplete understanding of the technical details in a firm’s technology selection can lead to a failure in the process of the technology opportunity discovery (TOD) and cause a series of R&D problems. This study proposes an approach for the automated TOD by combining the subject-section-object (SAO) and the generative topographic mapping (GTM), which concentrates on the role of the semantic information in TOD process. First, the semantic information of the technology components in a target field is extracted and the topics of different semantic structures are defined. Second, the GTM-based patent map is established to discover technology opportunities based on a vector matrix composed of patents and topics. Finally, the degree of semantic similarity is applied to measure the technology novelty and to identify promising technology opportunities. The case of the coal-bed methane extraction technology demonstrates that the automated approach based on the semantic information can help understand the concrete details of technology opportunities and improve the accuracy of TOD.

Remnant connectivity in pseudogapping: Experimental evidence for a direct generation approach

This paper reports the results of two acceptability judgment experiments that examine the effect of PP remnants with mismatching correlates in the antecedent clause (either a PP, with a distinct preposition, or an NP) on the acceptability of pseudogapping as well as non-elliptical controls. Across both experiments, three novel findings emerge: First, utterances with mismatching PPs across the ellipsis clause and its antecedent were consistently degraded relative to their preposition-matched counterparts. Second, this mismatch penalty arose for elliptical and non-elliptical variants alike with only minor differences between the two. Finally, a significant portion of the mismatch penalties was explained away by the degree of semantic similarity between the thematic relations established by the mismatching prepositions with respect to the antecedent verb which was measured in a separate norming experiment. We examine the consequences of these new empirical results for current theories of pseudogapping, namely (i) the remnant-raising analysis, according to which the remnant XP is raised leftward out of the VP prior to VP ellipsis, licensed under identity with its antecedent; and (ii) the direct generation analysis, under which auxiliaries are verbal proforms that recover their referent anaphorically without the need for remnant movement or syntactic identity between the verb and its antecedent. We conclude that the data are more naturally accounted for under the direct generation approach.

An empirical study of the semantic similarity of geospatial prepositions and their senses

ABSTRACT Spatial prepositions have been studied in some detail from multiple disciplinary perspectives. However, neither the semantic similarity of these prepositions, nor the relationships between the multiple senses of different spatial prepositions, are well understood. In an empirical study of 24 spatial prepositions, we identify the degree and nature of semantic similarity and extract senses for three semantically similar groups of prepositions using t-SNE, DBSCAN clustering, and Venn diagrams. We validate the work by manual annotation with another data set. We find nuances in meaning among proximity and adjacency prepositions, such as the use of close to instead of near for pairs of lines, and the importance of proximity over contact for the next to preposition, in contrast to other adjacency prepositions.

Impact of Online Education and Sentiment Analysis from Twitter Data using Topic Modeling Algorithms

During a pandemic, all industries suffer greatly, and every sector of the world suffers in some way, including the education sector. Internet expressions reflect users' feelings about a product or service. The polarity of information in source data toward a subject under investigation is determined by sentiment analysis processes. The goal of this study is to examine social media expressions about online teaching and learning, as online education will become a part of everyday life in the future. We collected data from Twitter using keywords related to online education and Google form from engineering undergraduate students for prototype implementation. This analysis will assist teachers, parents, and the student community in understanding the benefits and drawbacks of the education industry, allowing for further improvement in educational outcomes. We used aspect-based sentiment analysis and topic modeling to determine sentiment polarity and important topics for education sector stakeholders. To begin, we used TextBlob Python package to determine sentiment polarity, and Bag of Words, LDA and LSA model for discovering topics. After modeling topics from the collected data, topic Coherence is used to assess the degree of semantic similarity between high scoring words in the topic. The word cloud and LDAvis are used to visualize data. The experimental results are promising and it will assist education stakeholders in addressing the concerns that have been identified as social media expressions to work on. Since the boom in science and technology, humans have been trying to invent machines that could reduce their efforts in day to day activities. In this paper, we develop a personal assistant robot that could pick up objects and return it to the user. The robot is controlled using an android application in mobile phones. The robot can listen to user’s command and then respond in the best way possible. The user can command the robot to move to given location, capture images and pick objects. The robot is equipped with ultrasonic sensor and web camera that helps it to move to different location effectively. It is also equipped with sleds that play important role in object picking process. The robot uses a tiny YOLOv3 model which is rigorously trained on several images of the object. There are some possible improvements that can be achieved which could help this robot to be used in several other fields as well.

A New Alignment Word-Space Approach for Measuring Semantic Similarity for Arabic Text

This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.

Визначення ступеня семантичної подібності з використанням апарату дескриптивних логік

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of se­mantic similarity of the information allow ranking the found information on its semantic proximity to the pur­po­se or search request/template. Forming such measures should take into account many aspects from the mea­nings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such si­milarity functions, semantic ap­proaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

Investigating cross-linguistic similarity ratings: A Rasch analysis

A robust finding in psycholinguistics is that cognates and loanwords, which are words that typically share some degree of form and meaning across languages, provide the second language learner with benefits in language use when compared to words that do not share form and meaning across languages. This cognate effect has been shown to exist for Japanese learners of English; that is, words such as table are processed faster and more accurately in English because they have a loanword equivalent in Japanese (i.e., テーブル /te:buru/ ‘table’). Previous studies have also shown that the degree of phonological and semantic similarity, as measured on a numerical scale from ‘completely different’ to ‘identical’, also influences processing. However, there has been relatively little appraisal of such cross-linguistic similarity ratings themselves. Therefore, the present study investigated the structure of the similarity ratings using Rasch analysis, which is an analytic approach frequently used in the design and validation of language assessments. The findings showed that a 4-point scale may be optimal for phonological similarity ratings of cognates and a 2-point scale may be most appropriate for semantic similarity ratings. Furthermore, this study reveals that while a few raters and items misfitted the Rasch model, there was substantial agreement in ratings, especially for semantic similarity. The results validate the ratings for use in research and demonstrate the utility of Rasch analysis in the design and validation of research instruments in psychology.

Investigating cross-linguistic similarity ratings: A Rasch analysis

A robust finding in psycholinguistics is that cognates and loanwords, which are words that typically share some degree of form and meaning across languages, provide the second language learner with benefits in language use when compared to words that do not share form and meaning across languages. This cognate effect has been shown to exist for Japanese learners of English; that is, words such as table are processed faster and more accurately in English because they have a loanword equivalent in Japanese (i.e., テーブル /te:buru/ ‘table’). Previous studies have also shown that the degree of phonological and semantic similarity, as measured on a numerical scale from ‘completely different’ to ‘identical’, also influences processing. However, there has been relatively little appraisal of such cross-linguistic similarity ratings themselves. Therefore, the present study investigated the structure of the similarity ratings using Rasch analysis, which is an analytic approach frequently used in the design and validation of language assessments. The findings showed that a 4-point scale may be optimal for phonological similarity ratings of cognates and a 2-point scale may be most appropriate for semantic similarity ratings. Furthermore, this study reveals that while a few raters and items misfitted the Rasch model, there was substantial agreement in ratings, especially for semantic similarity. The results validate the ratings for use in research and demonstrate the utility of Rasch analysis in the design and validation of research instruments in psychology.

Defining degree of semantic similarity using description logic tools

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of semantic similarity of the information allow ranking the found information on its semantic proximity to the purpose or search request/template. Forming such measures should take into account many aspects from the meanings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such similarity functions, semantic approaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

Semantic Textual Similarity in Japanese Clinical Domain Texts Using BERT.

Background Semantic textual similarity (STS) captures the degree of semantic similarity between texts. It plays an important role in many natural language processing applications such as text summarization, question answering, machine translation, information retrieval, dialog systems, plagiarism detection, and query ranking. STS has been widely studied in the general English domain. However, there exists few resources for STS tasks in the clinical domain and in languages other than English, such as Japanese. Objective The objective of this study is to capture semantic similarity between Japanese clinical texts (Japanese clinical STS) by creating a Japanese dataset that is publicly available. Materials We created two datasets for Japanese clinical STS: (1) Japanese case reports (CR dataset) and (2) Japanese electronic medical records (EMR dataset). The CR dataset was created from publicly available case reports extracted from the CiNii database. The EMR dataset was created from Japanese electronic medical records. Methods We used an approach based on bidirectional encoder representations from transformers (BERT) to capture the semantic similarity between the clinical domain texts. BERT is a popular approach for transfer learning and has been proven to be effective in achieving high accuracy for small datasets. We implemented two Japanese pretrained BERT models: a general Japanese BERT and a clinical Japanese BERT. The general Japanese BERT is pretrained on Japanese Wikipedia texts while the clinical Japanese BERT is pretrained on Japanese clinical texts. Results The BERT models performed well in capturing semantic similarity in our datasets. The general Japanese BERT outperformed the clinical Japanese BERT and achieved a high correlation with human score (0.904 in the CR dataset and 0.875 in the EMR dataset). It was unexpected that the general Japanese BERT outperformed the clinical Japanese BERT on clinical domain dataset. This could be due to the fact that the general Japanese BERT is pretrained on a wide range of texts compared with the clinical Japanese BERT.

Weighted semantic plagiarism detection approach based on AHP decision model

ABSTRACT The increasing rate of academic plagiarism is a social problem that engages institutions and publishers. Plagiarists try to mislead the plagiarism detection system using synonyms and inverted word order. Numerous algorithms tried to overcome these problems using structural and semantic detection. However, most of them focus on overcoming some challenges. Moreover, all of them consider the same significant degree for all terms of the documents. On the other hand, the time complexity is an essential parameter that must be considered. This paper presents an effective way to detect structural and semantic similarity degrees among two papers only using some part of the paper’s content instead of all content, decreasing the time complexity. The similarity is calculated using a set of impressive terms and various combinations to augment plagiarism detection ability even if the word order is changed. Different weight is assigned to the word according to its position in various sections of the paper. Finally, an AHP (Analytical Hierarchy Process) model uses to calculate a weighted similarity. The results indicated that the proposed approach has more ability to detect semantic academic plagiarism, and the runtime is reduced compared to similar ones.

Semantic Discovery of Composite GIS Services

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.