Academic literature on the topic 'Lexical feature'
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Journal articles on the topic "Lexical feature"
Shaba, Varteen Hanna. "Lexical Simplification: a Universal Feature of Translation." Journal of Tikrit University for Humanities 28, no. 3, 1 (March 2, 2021): 1–22. http://dx.doi.org/10.25130/jtuh.28.3.1.2021.20.
Full textAmer, Abdullah Yahya Abdullah, and Tamanna Siddiqu. "A novel algorithm for sarcasm detection using supervised machine learning approach." AIMS Electronics and Electrical Engineering 6, no. 4 (2022): 345–69. http://dx.doi.org/10.3934/electreng.2022021.
Full textSaputra, Christian Eka, Derwin Suhartono, and Rini Wongso. "Question Categorization using Lexical Feature in Opini.id." ComTech: Computer, Mathematics and Engineering Applications 8, no. 4 (December 31, 2017): 229. http://dx.doi.org/10.21512/comtech.v8i4.4026.
Full textSlobodyan, Olena. "Native Geographical Terminology in Ukrainian East Slobozhansk Dialects of Luhansk Region (general structural and semantic characteristics)." Linguistics, no. 1 (42) (2020): 50–57. http://dx.doi.org/10.12958/2227-2631-2020-1-42-50-57.
Full textCairns, Charles E. "Phonotactics, markedness and lexical representation." Phonology 5, no. 2 (August 1988): 209–36. http://dx.doi.org/10.1017/s095267570000227x.
Full textBentea, Anamaria, and Stephanie Durrleman. "Person Matters: Relative Clauses in the Acquisition of French." Isogloss. Open Journal of Romance Linguistics 8, no. 5 (November 2, 2022): 1–16. http://dx.doi.org/10.5565/rev/isogloss.214.
Full textMondini, Sara, Eva Kehayia, Brendan Gillon, Giorgio Arcara, and Gonia Jarema. "Lexical access of mass and count nouns." Mental Lexicon 4, no. 3 (December 15, 2009): 354–79. http://dx.doi.org/10.1075/ml.4.3.03mon.
Full textCompton, Paul E., Peter Grossenbacher, Michael I. Posner, and Don M. Tucker. "A Cognitive-Anatomical Approach to Attention in Lexical Access." Journal of Cognitive Neuroscience 3, no. 4 (October 1991): 304–12. http://dx.doi.org/10.1162/jocn.1991.3.4.304.
Full textLodge, Ken. "Assimilation, deletion paths and underspecification." Journal of Linguistics 28, no. 1 (March 1992): 13–52. http://dx.doi.org/10.1017/s0022226700014985.
Full textAdger, David, and Jennifer Smith. "Variation in agreement: A lexical feature-based approach." Lingua 120, no. 5 (May 2010): 1109–34. http://dx.doi.org/10.1016/j.lingua.2008.05.007.
Full textDissertations / Theses on the topic "Lexical feature"
Hajro, Neira 1978. "Automated nasal feature detection for the lexical access from features project." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28401.
Full textIncludes bibliographical references (leaves 150-151).
The focus of this thesis was the design, implementation, and evaluation of a set of automated algorithms to detect nasal consonants from the speech waveform in a distinctive feature-based speech recognition system. The study used a VCV database of over 450 utterances recorded from three speakers, two male and one female. The first stage of processing for each speech waveform included automated 'pivot' estimation using the Consonant Landmark Detector - these 'pivots' were considered possible sonorant closures and releases in further analyses. Estimated pivots were analyzed acoustically for the nasal murmur and vowel-nasal boundary characteristics. For nasal murmur, the analyzed cues included observing the presence of a low frequency resonance in the short-time spectra, stability in the signal energy, and characteristic spectral tilt. The acoustic cues for the nasal boundary measured the change in the energy of the first harmonic and the net energy change of the 0-350Hz and 350-1000Hz frequency bands around the pivot time. The results of the acoustic analyses were translated into a simple set of general acoustic criteria that detected 98% of true nasal pivots. The high detection rate was partially offset by a relatively large number of false positives - 16% of all non-nasal pivots were also detected as showing characteristics of the nasal murmur and nasal boundary. The advantage of the presented algorithms is in their consistency and accuracy across users and contexts, and unlimited applicability to spontaneous speech.
by Neira Hajro.
M.Eng.
Zhang, Yong 1973. "Toward implementation of a feature-based lexical access system." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/17463.
Full textScheffel, Lucia. "Do feature importance and feature relevance differentially influence lexical semantic knowledge in individuals with aphasia?" Thesis, Kent State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3618930.
Full textThis study investigated two classifications of semantic features, feature importance and feature relevance, to verify if they differentially influence lexical semantic knowledge in individuals with aphasia. Feature importance is defined as "how important a feature is in defining a concept" (Hampton,1979), while feature relevance represents the "core meaning of a concept" (Sartori, Lombardi & Mattiuzi, 2005).
A sorting task was used with 20 volunteer participants with aphasia to investigate the semantic processing involved in the association of semantic features with nouns. A corpus of 18 nouns was displayed in front of each participant in groups of three along with a card containing the word "UNRELATED." The participants were given a deck of 18 cards containing features corresponding to the nouns and to the unrelated category, and were verbally instructed to sort the deck of cards into each of the four designated piles. The semantic features on the cards were rated as high, mid and low importance (HI, MI, LI) and high, mid and low relevance (HR, MR, LR).
Analysis was completed using a two-way between-subjects ANOVA to determine was whether the mean scores at the three different levels (e.g., low, mid and high) of importance and relevance differed, and to analyze if there was an interaction between the two classifications. The participants were able to assign high importance features with nouns more accurately than they did mid and low importance features. Feature relevance did not differentially influence noun-feature association. These results indicated that the ability of individuals with aphasia to accurately associate features with nouns is influenced by levels of feature importance.
In conclusion, this study found that individuals with aphasia are more cognitively sensitive to high level versus low level feature importance and the effect does not extend to a mid level of importance. The study also demonstrated that the levels of feature relevance did not differentially influence the ability of individuals with aphasia to associate semantic features with their appropriate nouns. Potential clinical implications and study limitations were discussed.
Scheffel, Lucia. "Do Feature Importance and Feature Relevance Differentially Influence Lexical Semantic Knowledge in Individuals with Aphasia?" Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1370971542.
Full textLamb, Katherine Marie. "Semantic feature distinctiveness and frequency." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4354.
Full textCree, 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.
Full textO’Donnell, Mick. "CORPUS LINGÜÍSTICO DE METASEMEMAS EN TEXTOS PERIODÍSTICOS DEL ESPAÑOL ACTUAL DE LA REGIÓN CENTRAL DE MÉXICO." Tesis de maestría, UNIVERSIDAD AUTÓNOMA DEL ESTADO DE MÉXICO, 2012. http://hdl.handle.net/20.500.11799/63907.
Full textTian, Leimin. "Recognizing emotions in spoken dialogue with acoustic and lexical cues." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31284.
Full textEk, Adam. "Blending Words or: How I Learned to Stop Worrying and Love the Blendguage : A computational study of lexical blending in Swedish." Thesis, Stockholms universitet, Avdelningen för datorlingvistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-160763.
Full textGENOVESE, GIULIANA. "L'infant-directed speech nella lingua italiana: caratteristiche lessicali, sintattiche, prosodiche e relazione con lo sviluppo linguistico." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241109.
Full textThis research work aims to explore infant-directed speech features in Italian language during the first year of an infant’s life and its effects on language acquisition, from precursors to advanced lexical and syntactic skills. The theoretical background assumes social bases of linguistic development. The first part consists of two studies on lexical, syntactic and prosodic properties in this special register. The second part includes two researches on quality and effects of linguistic input in language acquisition, taking into account a preverbal precursor and lexical and syntactic abilities during the second year of life; additionally, in this section, the predictors of language learning have been defined, exploring the role of linguistic input and the contribution of early communication skills in infants. The first study is a longitudinal design investigation, with an exhaustive analysis of lexical and syntactic characteristics of infant-directed speech in Italian language, comprehensive of both global and specific measures. From this investigation, the special register addressed to infants appears as a simplified but not simple with a period of maximum simplification in the second half of the first year of an infant’s life. The second longitudinal research examines prosodic properties in infant-directed speech and prosodic characterization of utterances with different pragmatic function. Results show how typical prosody in Italian infant-directed speech is overall emphasized in the preverbal period but, surprisingly, moderately; moreover, prosody changes during the first year even though without the same pattern of other non-tonal languages. Lastly, utterances with different pragmatic functions are characterized by a distinctive prosody. In the third contribution, predictors of language acquisition are longitudinally explored, analyzing the role of early communication skills in infants and of maternal input. In addition, input quality and stability are evaluated. About this topic, literature shows conflicting results. Overall, we find how subsequent linguistic abilities could be predicted by infant’s early communication skills and a by a rich, redundant, syntactically articulated but lexically repetitive input. Lastly, the fourth experimental work analyses the facilitator role of infant-directed song compared to infant-directed speech on the phonetic discrimination process, a preverbal precursor of language acquisition. Literature highlights how typical prosody in this special speech supports the identification of linguistic units in the verbal flow. Nevertheless, the role of infant-directed song has been poorly explored, especially as regard the development of a linguistic prerequisite. Main results prove a facilitator role of infant-directed song at the end of the first year of an infant’s life, when changes in the phonetic discrimination skill occur. Moreover, we find benefic effects of an higher musical and song exposition during the preverbal stage on both phonetic discrimination and subsequent lexical skills.
Books on the topic "Lexical feature"
National Institute of Standards and Technology (U.S.), ed. A lexical analogy to feature matching and pose estimation. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.
Find full textNational Institute of Standards and Technology (U.S.), ed. A lexical analogy to feature matching and pose estimation. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.
Find full textKornai, A. Lexical categories and x-bar features. Budapest: Akade miai Kiado, 1985.
Find full textAzova, Ol'ga, Elena D'yakova, Zhanna Antipova, and Mariya Vorob'eva. Speech therapy technologies. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1038017.
Full textMcDonald, Russ. ‘Pretty Rooms’. Edited by Jonathan Post. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199607747.013.0017.
Full textBárány, András. Differential object marking in Hungarian. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198804185.003.0002.
Full textHu, Xuhui. The syntax and semantics of Chinese resultatives. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808466.003.0004.
Full textMauranen, Anna. Second-Order Language Contact. Edited by Markku Filppula, Juhani Klemola, and Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.010.
Full textLamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.
Full textMusina, O. R., L. V. Timeeva, and I. V. Yarunina. E-learning English textbook on the topic "Infectious diseases, viruses". SIB-Expertise, 2021. http://dx.doi.org/10.12731/er0469.12072021.
Full textBook chapters on the topic "Lexical feature"
Perrin, Patrick, and Fred Petry. "Lexical Contextual Relations for the Unsupervised Discovery of Texts Features." In Feature Extraction, Construction and Selection, 157–73. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5725-8_10.
Full textBanik, Bireswar, and Abhijit Sarma. "Lexical Feature Based Feature Selection and Phishing URL Classification Using Machine Learning Techniques." In Communications in Computer and Information Science, 93–105. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6318-8_9.
Full textPeng, Keqin, Wenge Rong, Chen Li, Jiahao Hu, and Zhang Xiong. "Weight Aware Feature Enriched Biomedical Lexical Answer Type Prediction." In Neural Information Processing, 63–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_6.
Full textWu, Ziyu, Hongxu Hou, Ziyue Guo, Xuejiao Wang, and Shuo Sun. "Mongolian-Chinese Unsupervised Neural Machine Translation with Lexical Feature." In Lecture Notes in Computer Science, 334–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32381-3_27.
Full textPaukkeri, Mari-Sanna, Jaakko Väyrynen, and Antti Arppe. "Exploring Extensive Linguistic Feature Sets in Near-Synonym Lexical Choice." In Computational Linguistics and Intelligent Text Processing, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28601-8_1.
Full textKanerva, Jenna, Filip Ginter, Li-Hsin Chang, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, and Otto Tarkka. "Textual Paraphrase Dataset for Deep Language Modelling." In European Language Grid, 343–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_27.
Full textColla, Anna Maria, and Andrea Lorenzon. "Initial Letter Spotting as a Complementary Feature for Lexical Filtering of Cursive Words." In Perspectives in Neural Computing, 195–200. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0811-5_18.
Full textDeep, Paluck, Archita Mittal, Easha Pandey, and Sakshi Agarwal. "Lexical, Pragmatic and Linguistic Feature Based Two-Level Sarcasm Detection Using Machine Learning Techniques." In Advances in Intelligent Systems and Computing, 699–716. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2712-5_55.
Full textDey, Atanu, Mamata Jenamani, and Jitesh J. Thakkar. "Lexical TF-IDF: An n-gram Feature Space for Cross-Domain Classification of Sentiment Reviews." In Lecture Notes in Computer Science, 380–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_48.
Full textChoi, Maengsik, Junsoo Shin, and Harksoo Kim. "Lexical Feature Extraction Method for Classification of Erroneous Online Customer Reviews Based on Pattern Matching." In Lecture Notes in Electrical Engineering, 225–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40675-1_35.
Full textConference papers on the topic "Lexical feature"
Moates, Danny R., Zinny S. Bond, Russell Fox, and Verna Stockmal. "The feature [sonorant] in lexical access." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-758.
Full textAllaway, Emily, and Kathleen McKeown. "A Unified Feature Representation for Lexical Connotations." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.184.
Full textVéronis, Jean, and Nancy Ide. "A feature-based model for lexical databases." In the 14th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1992. http://dx.doi.org/10.3115/992133.992161.
Full textAare, Kätlin, Pärtel Lippus, and Juraj Šimko. "Creak as a Feature of Lexical Stress in Estonian." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1155.
Full textBlum, Aaron, Brad Wardman, Thamar Solorio, and Gary Warner. "Lexical feature based phishing URL detection using online learning." In the 3rd ACM workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1866423.1866434.
Full textGao, Kai, Hua Xu, Chengliang Gao, Hanyong Hao, Junhui Deng, and Xiaomin Sun. "Attention-Based BiLSTM Network with Lexical Feature for Emotion Classification." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489577.
Full textJyothi, Preethi, Karen Livescu, and Eric Fosler-Lussier. "Lexical access experiments with context-dependent articulatory feature-based models." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947454.
Full textCui, Xia, Sadamori Kojaku, Naoki Masuda, and Danushka Bollegala. "Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition." In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/s18-2030.
Full textStajner, Sanja, Simone Paolo Ponzetto, and Heiner Stuckenschmidt. "Automatic Assessment of Absolute Sentence Complexity." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/572.
Full textKoumpis, Konstantinos, Steve Renals, and Mahesan Niranjan. "Extractive summarization of voicemail using lexical and prosodic feature subset selection." In 7th European Conference on Speech Communication and Technology (Eurospeech 2001). ISCA: ISCA, 2001. http://dx.doi.org/10.21437/eurospeech.2001-560.
Full textReports on the topic "Lexical feature"
Horst, John. A lexical analogy to feature matching and pose estimation. Gaithersburg, MD: National Institute of Standards and Technology, 2002. http://dx.doi.org/10.6028/nist.ir.6790.
Full textRUDENKO, V., and E. KRASNOVA. FEATURES OF THE FORMATION OF THE TERM SYSTEM IN ASTROPHYSICS. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2077-1770-2021-13-4-3-117-125.
Full textBAGIYAN, A., and A. VARTANOV. SYSTEMS ACQUISITION IN MULTILINGUAL EDUCATION: THE CASE OF AXIOLOGICALLY CHARGED LEXIS. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2077-1770-2021-13-4-3-48-61.
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