Relevant bibliographies by topics / Semantic similarity metric

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Semantic similarity metric'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Semantic similarity metric"

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Parveen, Suraiya, and Ranjit Biswas. "An ontology-based semantic similarity metric to empower semantic search." International Journal of Engineering & Technology 7, no. 4 (2018): 2161. http://dx.doi.org/10.14419/ijet.v7i4.14153.

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Heterogeneity in documents is a challenge for information Retrieval. The keyword search focuses on matching the keywords with web repositories. It does not consider the synonyms or semantically similar words. The heterogeneity of the content makes retrieval inadequate. Semantic search helps to capture more appropriate results using domain ontology. Keyword search is extended with the help of similar concepts of ontology. Similarity between the ontological concepts is recognized to get appropriate search results. Once the semantic similarity among the concepts is known, more relevant documents
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Wang, Xiao-dong, Lei Guo, Jun Fang, and Shu-Fu Dong. "An EMD-Based Metric for Document Semantic Similarity." Journal of Electronics & Information Technology 30, no. 9 (2011): 2156–61. http://dx.doi.org/10.3724/sp.j.1146.2007.00177.

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Ming Che Lee, Jia Wei Chang, Tung Cheng Hsieh, Hui Hui Chen, and Ching Hui Chen. "A Sentence Similarity Metric Based on Semantic Patterns." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 18 (2012): 576–85. http://dx.doi.org/10.4156/aiss.vol4.issue18.71.

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Wang, Huibing, Lin Feng, Jing Zhang, and Yang Liu. "Semantic Discriminative Metric Learning for Image Similarity Measurement." IEEE Transactions on Multimedia 18, no. 8 (2016): 1579–89. http://dx.doi.org/10.1109/tmm.2016.2569412.

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Slam, Nady, Wushour Slamu, and Pei Wang. "A Case Representation and Similarity Measurement Model with Experience-Grounded Semantics." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (2020): 119–46. http://dx.doi.org/10.1142/s0218194020500060.

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Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-gro
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Hua, Yan, Yingyun Yang, and Jianhe Du. "Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval." Electronics 9, no. 3 (2020): 466. http://dx.doi.org/10.3390/electronics9030466.

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Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learnin
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Zhang, Pei Ying. "Semantic Similarity Metric and its Application in Text Classification." Applied Mechanics and Materials 170-173 (May 2012): 3711–14. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3711.

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Text classification is the task of assigning natural language textual documents to predefined categories based on their context. The main concern is this paper is to improve the accuracy of text classification system combined an improved CHI method and semantic similarity metric. Firstly, use an improved CHI method to select features from the raw features aim to reduce the dimensions of the features. Secondly, calculates the semantic distance between text feature vector and categorization feature vector so as to determine the document categorization. Finally, we carried out a series of experim
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Pirró, Giuseppe. "A semantic similarity metric combining features and intrinsic information content." Data & Knowledge Engineering 68, no. 11 (2009): 1289–308. http://dx.doi.org/10.1016/j.datak.2009.06.008.

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Stanchev, Lubomir. "Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph." International Journal of Semantic Computing 10, no. 04 (2016): 527–55. http://dx.doi.org/10.1142/s1793351x16400195.

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In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in them. Since our algorithm for semantic document clustering relies on multiple parameters, we examine how fine-tuning these values affects the quality of the result. Specifically, we use the Reuters-21578 b
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Zhou, Shiyuan, and Yinglin Wang. "Clustering Services Based on Community Detection in Service Networks." Mathematical Problems in Engineering 2019 (December 2, 2019): 1–11. http://dx.doi.org/10.1155/2019/1495676.

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Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. Therefore, it is of great importance to propose much more useful structure-related similarity metrics to impr
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Dissertations / Theses on the topic "Semantic similarity metric"

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Lewis, William D. "Measuring conceptual distance using WordNet: the design of a metric for measuring semantic similarity." University of Arizona Linguistics Circle, 2001. http://hdl.handle.net/10150/126645.

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This paper describes the development of a metric for measuring the semantic distance or similarity of words using the WordNet lexical database. Such a metric could be of use in development of search engines and text retrieval systems, tasks for which the richness of natural language can cause difficulty. Further, such a metric can prove invaluable to psycholinguists who wish to study lexical semantic similarity or speech errors (specifically malapropisms). The paper first explores an adjusted distance metric, a la Rada et al. 1989, and the problems such a metric presents. Additional analysis s
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Munbodh, Mrinal. "Deriving A Better Metric To Assess theQuality of Word Embeddings Trained OnLimited Specialized Corpora." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1601995854965902.

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Cheatham, Michelle Andreen. "The Properties of Property Alignment on the Semantic Web." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1407775249.

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Κοκόσης, Παύλος. "Εξόρυξη θεματικών αλυσίδων από ιστοσελίδες για την δημιουργία ενός θεματολογικά προσανατολισμένου προσκομιστή". 2005. http://nemertes.lis.upatras.gr/jspui/handle/10889/134.

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Οι θεματολογικά προσανατολισμένοι προσκομιστές είναι εφαρμογές που έχουν στόχο την συλλογή ιστοσελίδων συγκεκριμένης θεματολογίας από τον Παγκόσμιο Ιστό. Αποτελούν ένα ανοικτό ερευνητικό πεδίο των τελευταίων χρόνων. Σε αυτήν την διπλωματική εργασία επιχειρείται η υλοποίηση ενός θεματολογικά προσανατολισμένου προσκομιστή με χρήση λεξικών αλυσίδων. Οι λεξικές αλυσίδες είναι ένα σημαντικό λεξιλογικό και υπολογιστικό εργαλείο για την αναπαράσταση της έννοιας ενός κειμένου. Έχουν χρησιμοποιηθεί με επιτυχία στην αυτόματη δημιουργία περιλήψεων για κείμενα, αλλά και στην κατηγοριοποίησή τους σε θεματι
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Ferreira, João D. "Structural and semantic similarity metrics for chemical compound classification." Master's thesis, 2010. http://hdl.handle.net/10451/13866.

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Over the last few decades, there has been an increasing number of attempts at creating systems capable of comparing and classifying chemical compounds based on their structure and/or physicochemical properties. While the rate of success of these approaches has been increasing, particularly with the introduction of new and ever more sophisticated methods of machine learning, there is still room for improvement. One of the problems of these methods is that they fail to consider that similar molecules may have di erent roles in nature, or, to a lesser extend, that disparate molecules may have sim
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Ferreira, João Diogo Silva. "Structural and semantic similarity metrics for chemical compound classification." Master's thesis, 2010. http://hdl.handle.net/10451/5780.

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Tese de mestrado, Bioquímica, Universidade de Lisboa, Faculdade de Ciências, 2010<br>Ao longo das últimas décadas, tem-se assistido a um grande aumento na quantidade de dados produzidos e disponibilizados em química, em especial após a introdução de métodos de análise mecanizados. Devido a este crescimento no número de dados, existe cada vez mais uma necessidade de implementar sistemas automáticos computacionais capazes de armazenar, estudar e interpretar estes dados de forma eficiente. Uma das tarefas mais importantes em quimio-informática é, de facto, a utilização dos dados obtidos em labora
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Book chapters on the topic "Semantic similarity metric"

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Laverde, Natan A., Mirela T. Cazzolato, Agma J. M. Traina, and Caetano Traina. "Semantic Similarity Group By Operators for Metric Data." In Similarity Search and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68474-1_17.

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Meng, Lingling, Junzhong Gu, and Zili Zhou. "A Review of Information Content Metric for Semantic Similarity." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34595-1_42.

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Appling, Scott, and Erica Briscoe. "A Semantic Frame-Based Similarity Metric for Characterizing Technological Capabilities." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59888-8_8.

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Xia, Yu, Shuangbu Wang, Lihua You, and Jianjun Zhang. "Semantic Similarity Metric Learning for Sketch-Based 3D Shape Retrieval." In Computational Science – ICCS 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77977-1_5.

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Parveen, Suraiya, and Ranjit Biswas. "A Clinical Data Analytic Metric for Medical Ontology Using Semantic Similarity." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_46.

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Morales, Camilo, Diego Collarana, Maria-Esther Vidal, and Sören Auer. "MateTee: A Semantic Similarity Metric Based on Translation Embeddings for Knowledge Graphs." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60131-1_14.

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Nguyen, Phuong T., and Hong Anh Le. "Finding Similar Artists from the Web of Data: A PageRank Based Semantic Similarity Metric." In Future Data and Security Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26135-5_8.

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Pirró, Giuseppe, and Nuno Seco. "Design, Implementation and Evaluation of a New Semantic Similarity Metric Combining Features and Intrinsic Information Content." In On the Move to Meaningful Internet Systems: OTM 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88873-4_25.

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Ahlqvist, Ola. "Using Semantic Similarity Metrics to Uncover Category and Land Cover Change." In GeoSpatial Semantics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11586180_8.

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Jansen, Bart, Tran Duc Toan, and Frederik Temmermans. "Combining Image Similarity Metrics for Semantic Image Annotation." In On the Move to Meaningful Internet Systems: OTM 2012 Workshops. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33618-8_40.

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Conference papers on the topic "Semantic similarity metric"

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Subercaze, Julien, Christophe Gravier, and Frédérique Laforest. "On metric embedding for boosting semantic similarity computations." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-2002.

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Croft, David, Simon Coupland, Jethro Shell, and Stephen Brown. "A fast and efficient semantic short text similarity metric." In 2013 13th UK Workshop on Computational Intelligence (UKCI). IEEE, 2013. http://dx.doi.org/10.1109/ukci.2013.6651309.

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Wu, Minghui, Fanwei Zhu, Jia Lv, Tao Jiang, and Jing Ying. "Improve Semantic Web Services Discovery through Similarity Search in Metric Space." In 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering (TASE). IEEE, 2009. http://dx.doi.org/10.1109/tase.2009.50.

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Yonghe, Chu, Hongfei Lin, Liang Yang, Yufeng Diao, Shaowu Zhang, and Fan Xiaochao. "Refining Word Representations by Manifold Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/749.

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Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifo
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Lo, Chi-kiu, and Michel Simard. "Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data." In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/k19-1020.

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Pino, Omar Vidal, Erickson R. Nascimento, and Mario F. M. Campos. "Semantic Description of Objects in Images Based on Prototype Theory." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12994.

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This research aims to build a model for the semantic description of objects based on visual features extracted from images. We introduce a novel semantic description approach inspired by the Prototype Theory. Inspired by the human approach used to represent categories, we propose a novel Computational Prototype Model (CPM) that encodes and stores the object’s image category’s central semantic meaning: the semantic prototype. Our CPM model represents and constructs the semantic prototypes of object categories using Convolutional Neural Networks (CNN). The proposed Prototype-based Description Mo
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Lo, Chi-kiu, Michel Simard, Darlene Stewart, Samuel Larkin, Cyril Goutte, and Patrick Littell. "Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task." In Proceedings of the Third Conference on Machine Translation: Shared Task Papers. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-6481.

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Sexton, Thurston, and Mark Fuge. "Using Semantic Fluency Models Improves Network Reconstruction Accuracy of Tacit Engineering Knowledge." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98429.

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Abstract Human- or expert-generated records that describe the behavior of engineered systems over a period of time can be useful for statistical learning techniques like pattern detection or output prediction. However, such data often assumes familiarity of a reader with the relationships between entities within the system — that is, knowledge of the system’s structure. This required, but unrecorded “tacit” knowledge makes it difficult to reliably learn patterns of system behavior using statistical modeling techniques on these written records. Part of this difficulty stems from a lack of good
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Banerjee, Sayan, Avik Hati, Subhasis Chaudhuri, and Rajbabu Velmurugan. "CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/95.

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The objective in image co-segmentation is to jointly segment unknown common objects from a given set of images. In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model. It is composed of a metric learning and decision network leading to a novel conditional siamese encoder-decoder network for estimating a co-segmentation mask. The role of the metric learning network is to find an optimum latent feature space where objects of the same class are closer and that of different classes are separated by a certain margin. Depending on the extracted featu
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Zhang, Hainan, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. "Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/635.

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Sequence to sequence (Seq2Seq) approach has gained great attention in the field of single-turn dialogue generation. However, one serious problem is that most existing Seq2Seq based models tend to generate common responses lacking specific meanings. Our analysis show that the underlying reason is that Seq2Seq is equivalent to optimizing Kullback–Leibler (KL) divergence, thus does not penalize the case whose generated probability is high while the true probability is low. However, the true probability is unknown, which poses challenges for tackling this problem. Inspired by the fact that the coh
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