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Relevant bibliographies by topics / Lexical similarity

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Academic literature on the topic 'Lexical similarity'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Lexical similarity"

1

Barbero, Chiara, and Raquel Amaro. "Are We Talking about the Same Thing? Modeling Semantic Similarity between Common and Specialized Lexica in WordNet." Languages 9, no. 3 (2024): 89. http://dx.doi.org/10.3390/languages9030089.

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Specialized languages can activate different sets of semantic features when compared to general language or express concepts through different words according to the domain. The specialized lexicon, i.e., lexical units that denote more specific concepts and knowledge emerging from specific domains, however, co-exists with the common lexicon, i.e., the set of lexical units that denote concepts and knowledge shared by the average speakers, regardless of their specific training or expertise. Communication between specialists and non-specialists can show a big gap between language(s), and therefor

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Dam, Helle V. "Lexical Similarity vs Lexical Dissimilarity in Consecutive Interpreting." Translator 4, no. 1 (1998): 49–68. http://dx.doi.org/10.1080/13556509.1998.10799006.

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Sanker, Chelsea. "Lexical ambiguity and acoustic distance in discrimination." Proceedings of the Linguistic Society of America 5, no. 1 (2020): 431. http://dx.doi.org/10.3765/plsa.v5i1.4719.

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This work presents a perceptual study on how acoustic details and knowledge of the lexicon influence discrimination decisions. English-speaking listeners were less likely to identify phonologically matching items as the same when they differed in vowel duration, but differences in mean F0 did not have an effect. Although both are components of English contrasts, the results only provide evidence for attention to vowel duration as a potentially contrastive cue. Lexical ambiguity was a predictor of response time. Pairs with matching duration were identified more quickly than pairs with distinct

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KOTLERMAN, LILI, IDO DAGAN, IDAN SZPEKTOR, and MAAYAN ZHITOMIRSKY-GEFFET. "Directional distributional similarity for lexical inference." Natural Language Engineering 16, no. 4 (2010): 359–89. http://dx.doi.org/10.1017/s1351324910000124.

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AbstractDistributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion. We identify desired properties of such measures for lexical inference, specify a particular measure based on Average Precision that addresses thes

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Johns, Brendan T., and Michael N. Jones. "Perceptual Inference Through Global Lexical Similarity." Topics in Cognitive Science 4, no. 1 (2012): 103–20. http://dx.doi.org/10.1111/j.1756-8765.2011.01176.x.

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Shatz, Itamar, Theodora Alexopoulou, and Akira Murakami. "The potential influence of cross-linguistic lexical similarity on lexical diversity in L2 English writing." Corpora 19, no. 2 (2024): 131–56. http://dx.doi.org/10.3366/cor.2024.0305.

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We examined the potential influence of L1–L2 lexical similarity on L2 lexical diversity, to determine whether the robust facilitative effect of lexical similarity that is found in processing and broad learning outcomes extends to this measure of L2 production. Our sample included two matching learner sub-corpora, containing 8,500 and 6,390 English texts, written in response to ninety-five and seventy-one writing tasks, by speakers of nine typologically diverse L1s, in the A1–B2 cefr range of L2 English proficiency. We found that lexical similarity did not influence L2 lexical diversity at any

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Rai, Tara Mani. "Unraveling the Relationship among the Kirati Languages." Nepalese Linguistics 38, no. 1 (2024): 91–98. http://dx.doi.org/10.3126/nl.v38i1.71561.

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This paper explores the relationships among the Kirati languages through lexical comparisons. The analysis, employing the Swadesh 100-word list, shows that Bantawa and Puma as well as Mugali and Phangduwali exhibit the highest lexical similarity, at 52%, while Yakkha and Koits-Sunuwar have the lowest similarity, at just 1%. In terms of phonetic similarity, Bantawa and Puma also show the greatest resemblance, with a similarity rate of up to 68%, whereas Mugali and Wambule show the least similarity, at 34%. These findings reveal that the lexical similarities and differences among the Kirati lang

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Pakray, Partha, Sivaji Bandyopadhyay, and Alexander Gelbukh. "Textual Entailment Using Lexical And Syntactic Similarity." International Journal of Artificial Intelligence & Applications 2, no. 1 (2011): 43–58. http://dx.doi.org/10.5121/ijaia.2011.2104.

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Zimack, Liza K., James R. Sawusch, Kathleen M. Measer, Paul A. Luce, and Rochelle S. Newman. "Talker voice and similarity affect lexical neighborhoods." Journal of the Acoustical Society of America 108, no. 5 (2000): 2479. http://dx.doi.org/10.1121/1.4743146.

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Steriade, Donca. "Knowledge of Similarity and Narrow Lexical Override." Annual Meeting of the Berkeley Linguistics Society 29, no. 1 (2003): 583. http://dx.doi.org/10.3765/bls.v29i1.989.

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Proceedings of the Twenty-Ninth Annual Meeting of the Berkeley Linguistics Society: General Session and Parasession on Phonetic Sources of Phonological Patterns: Synchronic and Diachronic Explanations (2003)

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Dissertations / Theses on the topic "Lexical similarity"

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Baker, Kirk. "Multilingual Distributional Lexical Similarity." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1221752517.

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Weeds, Julie Elizabeth. "Measures and applications of lexical distributional similarity." Thesis, University of Sussex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398753.

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Jiang, Jian. "Lexical semantic similarity and its application to business catalog retrieval." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ32834.pdf.

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Ellman, Jeremy. "Using Roget's thesaurus to determine the similarity of texts." Thesis, University of Sunderland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324394.

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Gorbunova, Anastasia A. "A Metric for Orthographic Similarity: Theory and Implications." Thesis, The University of Arizona, 2007. http://hdl.handle.net/10150/193269.

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Letter position plays an important role in lexical access. But are some positions more important than the others? Findings from numerous studies support the notion that in lexical access, initial letters produce strongest activation, which weakens towards the end of the word. In order to create a metric for computing the activation produced by each letter position in a correctly spelled word versus a word in which some or all letters are transposed, the formula for calculating a word's orthographic match coefficient (OMC) was developed and tested. Utilizing the masked priming paradigm and a le

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Cree, George S. "An attractor model of lexical conceptual processing, statistical feature relationships and semantic similarity priming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0001/MQ30733.pdf.

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ALTURKI, Fadwi Waleed. "LEXICAL KNOWLEDGE OF VERB-PARTICLE BY SAUDI ENGLISH LEARNERS." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/theses/1609.

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Verb-particle constructions are one of the most complex components of the English language. Understanding and producing such difficult constructs in a second language (L2) is a challenge for L2 learners of English. This research was based on the study by Blais and Gonnerman (2013). The purpose of the current study was to measure American and Saudi participants' sensitivity to the degree of semantic similarity between verb/verb-particle constructions. The survey of similarity ratings was administered to 107 American native English speakers and 67 Saudi English learners. The participants were as

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Chiu, Pei-Wen Andy. "From Atoms to the Solar System: Generating Lexical Analogies from Text." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2943.

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A <em>lexical analogy</em> is two pairs of words (<em>w</em><sub>1</sub>, <em>w</em><sub>2</sub>) and (<em>w</em><sub>3</sub>, <em>w</em><sub>4</sub>) such that the relation between <em>w</em><sub>1</sub> and <em>w</em><sub>2</sub> is identical or similar to the relation between <em>w</em><sub>3</sub> and <em>w</em><sub>4</sub>. For example, (<em>abbreviation</em>, <em>word</em>) forms a lexical analogy with (<em>abstract</em>, <em>report</em>), because in both cases the former is a shortened version of the latter. Lexical analogies are of theoretic interest because they represent a seco

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Utgof, Darja. "The Perception of Lexical Similarities Between L2 English and L3 Swedish." Thesis, Linköping University, Department of Culture and Communication, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15874.

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<p>The present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition.</p><p>It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of educati

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Matikainen, Tiina Johanna. "Semantic Representation of L2 Lexicon in Japanese University Students." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/133319.

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CITE/Language Arts<br>Ed.D.<br>In a series of studies using semantic relatedness judgment response times, Jiang (2000, 2002, 2004a) has claimed that L2 lexical entries fossilize with their equivalent L1 content or something very close to it. In another study using a more productive test of lexical knowledge (Jiang 2004b), however, the evidence for this conclusion was less clear. The present study is a partial replication of Jiang (2004b) with Japanese learners of English. The aims of the study are to investigate the influence of the first language (L1) on second language (L2) lexical knowledge

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Books on the topic "Lexical similarity"

1

Weeds, Julie Elizabeth. Measures and applications of lexical distributional similarity. 2004.

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2

You, Liming. The role of cross-linguistic lexical similarity in the use of motion verbs in English by Chinese and Japanese learners. 1996.

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3

Olson, Kenneth S., and M. Paul Lewis. The Ethnologue and L2 Mapping. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190657543.003.0003.

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The early focus of the Ethnologue was on L1 use and is reflected in the maps that are included with each new edition. Typically, the maps show locations and boundaries corresponding to the distribution of L1 speakers. The location of widespread, second, or additional languages (such as national languages, lingua francas, and languages of wider communication) is only occasionally represented by maps, using a variety of methods. Major factors affecting this effort are related to language identification (ISO 639-3), categorization (status: sociohistorical, official recognition, vitality), and ana

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Pietroski, Paul M. Locating meanings. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812722.003.0002.

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This chapter characterizes meanings in terms of certain generative procedures. We can begin to locate the natural phenomenon of linguistic meaning by focusing on (Chomsky-style) examples of constrained homophony. Two or more lexical items can connect distinct meanings with the same pronunciation; and phrases like ‘ready to please’ are similarly homophonous. But as ‘eager to please’ and ‘easy to please’ illustrate, phrasal homophony is constrained. Such facts provide important clues about what meanings are, and how they can(not) be combined. The details provide reasons for identifying the langu

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Kelly, Piers. The Last Language on Earth. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197509913.001.0001.

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The Eskayan language of Bohol in the southern Philippines has been an object of controversy ever since it came to light in the early 1980s. Written in an unusual script, Eskayan bears no obvious similarity to any known language of the Philippines, a fact that has prompted speculation that it was either displaced from afar, fossilized from the deep past, or invented as an elaborate hoax. This book investigates the history of Eskayan through a systematic review of its writing system, grammar, and lexicon and carefully evaluates written and oral narratives provided by its contemporary speakers. T

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Book chapters on the topic "Lexical similarity"

1

Majumder, Goutam, Partha Pakray, and David Eduardo Pinto Avendaño. "Interpretable Semantic Textual Similarity Using Lexical and Cosine Similarity." In Social Transformation – Digital Way. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_59.

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Zhang, Xianchao, Wen Xu, and Wenxin Liang. "Extracting Local Web Communities Using Lexical Similarity." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14589-6_33.

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Bui, Van-Tan, and Phuong-Thai Nguyen. "Measuring Semantic Similarity of Vietnamese Sentences Based on Lexical and Distribution Similarity." In Modelling, Computation and Optimization in Information Systems and Management Sciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92666-3_22.

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Che Alhadi, Arifah, Aziz Deraman, Masita@Masila Abdul Jalil, Wan Nural Jawahir Wan Yussof, and Shahrul Azman Mohd Noah. "Short Text Computing Based on Lexical Similarity Model." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30275-7_27.

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Wali, Wafa, Bilel Gargouri, and Abdelmajid Ben Hamadou. "Using Standardized Lexical Semantic Knowledge to Measure Similarity." In Knowledge Science, Engineering and Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12096-6_9.

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Kunchukuttan, Anoop, and Pushpak Bhattacharyya. "Utilizing Lexical Similarity by Using Subword Translation Units." In Machine Translation and Transliteration Involving Related and Low-resource Languages. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003096771-4.

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Singh, Jagendra, and Aditi Sharan. "Lexical Ontology-Based Computational Model to Find Semantic Similarity." In Intelligent Computing, Networking, and Informatics. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1665-0_12.

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Carroll, John, Rob Koeling, and Shivani Puri. "Lexical Acquisition for Clinical Text Mining Using Distributional Similarity." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28601-8_20.

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Gupta, Yogesh, and Amit Saraswat. "Semantic Similarity Computation Among Hindi Words Using Hindi Lexical Ontology." In Intelligent Computing Techniques for Smart Energy Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_25.

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Shin, Hyopil, and Insik Cho. "A Noun-Predicate Bigram-Based Similarity Measure for Lexical Relations." In Advances in Natural Language Processing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85287-2_43.

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Conference papers on the topic "Lexical similarity"

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Al-Saiyd, Nedhal A., and Intisar A. Al-Sayed. "Semantic Textual Similarity (STS) in Arabic using Lexical-Semantic Analysis." In 2024 25th International Arab Conference on Information Technology (ACIT). IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877260.

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Han, Chaohui. "Lexical Similarity Calculation in English Translation Based on Multi-Type Word Embedding Fusion." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11021820.

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Karidi, Taelin, Eitan Grossman, and Omri Abend. "Aligning Alignments: Do Colexification and Distributional Similarity Align as Measures of cross-lingual Lexical Alignment?" In Proceedings of the 28th Conference on Computational Natural Language Learning. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.conll-1.26.

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Maurya, Kaushal, Rahul Kejriwal, Maunendra Desarkar, and Anoop Kunchukuttan. "CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages." In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.eacl-short.26.

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Kotlerman, Lili, Ido Dagan, Idan Szpektor, and Maayan Zhitomirsky-Geffet. "Directional distributional similarity for lexical expansion." In the ACL-IJCNLP 2009 Conference Short Papers. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1667583.1667606.

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Weeds, Julie, David Weir, and Diana McCarthy. "Characterising measures of lexical distributional similarity." In the 20th international conference. Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1220355.1220501.

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Ayeldeen, Heba, Aboul Ella Hassanien, and Aly A. Fahmy. "Lexical similarity using fuzzy Euclidean distance." In 2014 International Conference on Engineering and Technology (ICET). IEEE, 2014. http://dx.doi.org/10.1109/icengtechnol.2014.7016801.

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Wiranata, Dony Arisandy, Moch Arif Bijaksana, and Mohamad Syahrul Mubarok. "Quranic Concepts Similarity Based on Lexical Database." In 2018 6th International Conference on Information and Communication Technology (ICoICT). IEEE, 2018. http://dx.doi.org/10.1109/icoict.2018.8528794.

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Erbs, Nicolai, Iryna Gurevych, and Torsten Zesch. "Sense and Similarity: A Study of Sense-level Similarity Measures." In Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014). Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/s14-1004.

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Stevenson, Mark. "Augmenting noun taxonomies by combining lexical similarity metrics." In the 19th international conference. Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1072228.1072266.

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