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Relevant bibliographies by topics / Language identification

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Academic literature on the topic 'Language identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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Journal articles on the topic "Language identification"

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Suganthi, Mrs Dr V., C. Thavapriya, and T. Mirudhu Bashini. "Sign Language Identification." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 5997–6001. http://dx.doi.org/10.55248/gengpi.5.0324.0855.

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Kumar, P. Vijay, and A. Raviteja A. Raviteja. "Automatic Indian Language Identification." International Journal of Scientific Research 2, no. 4 (June 1, 2012): 79–82. http://dx.doi.org/10.15373/22778179/apr2013/31.

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MALMASI, SHERVIN, and MARK DRAS. "Multilingual native language identification." Natural Language Engineering 23, no. 2 (December 2, 2015): 163–215. http://dx.doi.org/10.1017/s1351324915000406.

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AbstractWe present the first comprehensive study of Native Language Identification (NLI) applied to text written in languages other than English, using data from six languages. NLI is the task of predicting an author’s first language using only their writings in a second language, with applications in Second Language Acquisition and forensic linguistics. Most research to date has focused on English but there is a need to apply NLI to other languages, not only to gauge its applicability but also to aid in teaching research for other emerging languages. With this goal, we identify six typologically very different sources of non-English second language data and conduct six experiments using a set of commonly used features. Our first two experiments evaluate our features and corpora, showing that the features perform well and at similar rates across languages. The third experiment compares non-native and native control data, showing that they can be discerned with 95 per cent accuracy. Our fourth experiment provides a cross-linguistic assessment of how the degree of syntactic data encoded in part-of-speech tags affects their efficiency as classification features, finding that most differences between first language groups lie in the ordering of the most basic word categories. We also tackle two questions that have not previously been addressed for NLI. Other work in NLI has shown that ensembles of classifiers over feature types work well and in our final experiment we use such an oracle classifier to derive an upper limit for classification accuracy with our feature set. We also present an analysis examining feature diversity, aiming to estimate the degree of overlap and complementarity between our chosen features employing an association measure for binary data. Finally, we conclude with a general discussion and outline directions for future work.

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Qafmolla, Nejla. "Automatic Language Identification." European Journal of Language and Literature 7, no. 1 (January 21, 2017): 140. http://dx.doi.org/10.26417/ejls.v7i1.p140-150.

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Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID – so as to generate more efficient systems – as well as major challenges that are still in the eye of the storm of this field. Despite the fact that the current approaches have accomplished considerable success, future research concerning some issues remains on the table. The aim of this paper shall not be to describe the historic background of this field of studies, but rather to provide an overview of the current state of LID systems, as well as to classify the approaches developed to accomplish them. LID systems have advanced and are continuously evolving. Some of the issues that need special attention and improvement are semantics, the identification of various dialects and varieties of a language, identification of spelling errors, data retrieval, multilingual documents, MT and speech-to-speech translation. Methods applied to date have been good from a technical point of view, but not from a semantic one.

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Zissman, Marc A., and Kay M. Berkling. "Automatic language identification." Speech Communication 35, no. 1-2 (August 2001): 115–24. http://dx.doi.org/10.1016/s0167-6393(00)00099-6.

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Van Segbroeck, Maarten, Ruchir Travadi, and Shrikanth S. Narayanan. "Rapid Language Identification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 7 (July 2015): 1118–29. http://dx.doi.org/10.1109/taslp.2015.2419978.

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Ranasinghe, Tharindu, and Marcos Zampieri. "Multilingual Offensive Language Identification for Low-resource Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–13. http://dx.doi.org/10.1145/3457610.

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Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We project predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi, Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in TRAC-2 shared task [23], 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in OffensEval 2020 [58], 0.8568 F1 macro for Hindi in HASOC 2019 shared task [27], and 0.7513 F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) [7], showing that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages. Additionally, we report competitive performance on Arabic and Turkish using the training and development sets of OffensEval 2020 shared task. The results for all languages confirm the robustness of cross-lingual contextual embeddings and transfer learning for this task.

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Botha, G., V. Zimu, and E. Barnard. "Text-based language identification for south african languages." SAIEE Africa Research Journal 98, no. 4 (December 2007): 141–46. http://dx.doi.org/10.23919/saiee.2007.9485636.

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Jothilakshmi, S., V. Ramalingam, and S. Palanivel. "A hierarchical language identification system for Indian languages." Digital Signal Processing 22, no. 3 (May 2012): 544–53. http://dx.doi.org/10.1016/j.dsp.2011.11.008.

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Baimyrza, A. "LANGUAGE IDENTIFICATION PROCESSES OF THE YOUTH." Tiltanym, no. 3 (September 30, 2021): 28–36. http://dx.doi.org/10.55491/2411-6076-2021-3-28-36.

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The article deals with the role of the Russian language in the processes of language identification of students. The results of the sociolinguistic survey, the main objectives of which were determined based on the need to obtain information on the following aspects of the language situation: the degree of knowledge of the youth in the state, Russian and other languages; the level and nature of social preferences in relation to the use of languages in various spheres of life; the nature of social and language preferences of the young population. The review of theoretical works of Kazakhstan and foreign scientists on this subject is given. The conclusions of the study noted the significant role of the Russian language in the formation of linguistic identity, which is due not only to historical realities, in particular, the language policy conducted for a long time, as well as the conscious choice of language. The conducted studies prove that language proficiency and its use are a factor of socialization of young people and determine the style of human interaction with their social environment.

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Dissertations / Theses on the topic "Language identification"

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Botha, Gerrti Reinier. "Text-based language identification for the South African languages." Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-090942008-133715/.

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Yin, Bo Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Language identification with language and feature dependency." Awarded By:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44045.

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The purpose of Language Identification (LID) is to identify a specific language from a spoken utterance, automatically. Language-specific characteristics are always associated with different languages. Most existing LID approaches utilise a statistical modelling process with common acoustic/phonotactic features to model specific languages while avoiding any language-specific knowledge. Great successes have been achieved in this area over past decades. However, there is still a huge gap between these languageindependent methods and the actual language-specific patterns. It is extremely useful to address these specific acoustic or semantic construction patterns, without spending huge labour on annotation which requires language-specific knowledge. Inspired by this goal, this research focuses on the language-feature dependency. Several practical methods have been proposed. Various features and modelling techniques have been studied in this research. Some of them carry out additional language-specific information without manual labelling, such as a novel duration modelling method based on articulatory features, and a novel Frequency-Modulation (FM) based feature. The performance of each individual feature is studied for each of the language-pair combinations. The similarity between languages and the contribution in identifying a language by using a particular feature are defined for the first time, in a quantitative style. These distance measures and languagedependent contributions become the foundations of the later-presented frameworks ?? language-dependent weighting and hierarchical language identification. The latter particularly provides remarkable flexibility and enhancement when identifying a relatively large number of languages and accents, due to the fact that the most discriminative feature or feature-combination is used when separating each of the languages. The proposed systems are evaluated in various corpora and task contexts including NIST language recognition evaluation tasks. The performances have been improved in various degrees. The key techniques developed for this work have also been applied to solve a different problem other than LID ?? speech-based cognitive load monitoring.

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Newman, Jacob Laurence. "Language identification using visual features." Thesis, University of East Anglia, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539371.

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Berkling, Kay Margarethe. "Automatic language identification with sequences of language-independent phoneme clusters /." Full text open access at:, 1996. http://content.ohsu.edu/u?/etd,204.

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Conti, Matteo. "Machine Learning Based Programming Language Identification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20875/.

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L'avvento dell'era digitale ha contribuito allo sviluppo di nuovi settori tecnologici, i quali, per diretta conseguenza, hanno portato alla richiesta di nuove figure professionali capaci di assumere un ruolo chiave nel processo d'innovazione tecnologica. L'aumento di questa richiesta ha interessato particolarmente il settore dello sviluppo del software, a seguito della nascita di nuovi linguaggi di programmazione e nuovi campi a cui applicarli. La componente principale di cui è composto un software, infatti, è il codice sorgente, il quale può essere rappresentato come un archivio di uno o più file testuali contenti una serie d'istruzioni scritte in uno o più linguaggi di programmazione. Nonostante molti di questi vengano utilizzati in diversi settori tecnologici, spesso accade che due o più di questi condividano una struttura sintattica e semantica molto simile. Chiaramente questo aspetto può generare confusione nell'identificazione di questo all'interno di un frammento di codice, soprattutto se consideriamo l'eventualità che non sia specificata nemmeno l'estensione dello stesso file. Infatti, ad oggi, la maggior parte del codice disponibile online contiene informazioni relative al linguaggio di programmazione specificate manualmente. All'interno di questo elaborato ci concentreremo nel dimostrare che l'identificazione del linguaggio di programmazione di un file `generico' di codice sorgente può essere effettuata in modo automatico utilizzando algoritmi di Machine Learning e non usando nessun tipo di assunzione `a priori' sull'estensione o informazioni particolari che non riguardino il contenuto del file. Questo progetto segue la linea dettata da alcune ricerche precedenti basate sullo stesso approccio, confrontando tecniche di estrazione delle features differenti e algoritmi di classificazione con caratteristiche molto diverse, cercando di ottimizzare la fase di estrazione delle features in base al modello considerato.

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Munday, Emma Rachel. "Language and identification in contemporary Kazakhstan." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/6200.

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In the years since the dissolution of the Soviet Union Central Asia has experienced wide-reaching and ongoing social change. The structures and values of all social strata have been questioned and re-evaluated in a continuing exploration of what it means to be part of the post-Soviet space. Within this space, identity formation and reformation has been a pre-eminent process for individuals, for groups of all kinds and for the newly emerging states and their leaders. Through the analysis of individual interviews and selected newspaper extracts and government policy documents this study explores the ways in which ethnic and state identities are being negotiated in Kazakhstan. Using the social identity theory framework it investigates the value and content of these identities by examining the state ideologies of language and the policies which are their expression as well as the discourses of language and identity engaged in by individuals and in the media. There is an exploration of common and conflicting themes referred to as aspects of these identities, of outgroups deemed relevant for comparison and of the roles of Kazakh and Russian in particular, alongside other languages, in relation to these identities. The study focuses on the availability to an individual of multiple possible identities of differing levels of inclusiveness. The saliency of a particular identity is demonstrated to vary according both to context and to the beliefs and goals of the individual concerned. The importance of discourse to processes of identity formation and maintenance is also described and the interaction between discourse and social context is highlighted. The ongoing construction of a Kazakhstani identity is described and the importance of group norms of hospitality, inclusiveness and interethnic accord observed. The sense of learning from other cultures and of mutual enrichment is also demonstrated. However, these themes exist in tension with those of Kazakhstan as belonging primarily to Kazakhs and of cultural oppression and loss. The multi-dimensional nature of ethnic identity is highlighted as is the difficulty, experienced by some, in maintaining a positive sense of ethnic group identity. Perceptions of the importance of language in the construction of ethnic and state identity are explored as are the tensions created by the ideological and instrumental values adhering to different languages in use in Kazakhstan.

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Nkadimeng, Calvin. "Language identification using Gaussian mixture models." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4170.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous.
AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger.

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Avenberg, Anna. "Automatic language identification of short texts." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-421032.

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The world is growing more connected through the use of online communication, exposing software and humans to all the world's languages. While devices are able to understand and share the raw data between themselves and with humans, the information itself is not expressed in a monolithic format. This causes issues both in the human to computer interaction and human to human communication. Automatic language identification (LID) is a field within artificial intelligence and natural language processing that strives to solve a part of these issues by identifying languages from text, sign language and speech. One of the challenges is to identify the short pieces of text that can be found online, such as messages, comments and posts on social media. This is due to the small amount of information they carry. The goal of this thesis has been to build a machine learning model that can identify the language for these short pieces of text. A long short-term memory (LSTM) machine learning model was built and benchmarked towards Facebook's fastText model. The results show how the LSTM model reached an accuracy of around 95% and the fastText model used as comparison reached an accuracy of 97%. The LSTM model struggled more when identifying texts shorter than 50 characters than with longer text. The classification performance of the LSTM model was also relatively poor in cases where languages were similar, like Croatian and Serbian. Both the LSTM model and the fastText model reached accuracy's above 94% which can be considered high, depending on how it is evaluated. There are however many improvements and possible future work to be considered; looking further into texts shorter than 50 characters, evaluating the model's softmax output vector values and how to handle similar languages.

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Foran, Jeffrey (Jeffrey Matthew) 1977. "Missing argument referent identification in natural language." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80532.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 54-55).
by Jeffrey Foran.
S.B.and M.Eng.

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Gambardella, Maria-Elena. "Cleartext detection and language identification in ciphers." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446439.

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In historical cryptology, cleartext represents text written in a known language ina cipher (a hand-written manuscript aiming at hiding the content of a message).Cleartext can give us an historical interpretation and contextualisation of themanuscript and could help researchers in cryptanalysis, but to these days thereis still no research on how to automatically detect cleartext and identifying itslanguage. In this paper, we investigate to what extent we can automaticallydistinguish cleartext from ciphertext in transcribed historical ciphers and towhat extent we are able to identify its language. We took a rule-based approachand run 7 different models using historical language models on ciphertextsprovided by the DECRYPT-Project. Our results show that using unigrams andbigrams on a word-level combined with 3-grams, 4-grams and 5-grams on acharacter-level is the best approach to tackle cleartext detection.

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Books on the topic "Language identification"

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United States. Bureau of Justice Assistance and Ohio. Office of Criminal Justice Services (1993- ), eds. I speak--: Language identification guide. 2nd ed. Washington, DC: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Assistance, 2006.

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Jauhiainen, Tommi, Marcos Zampieri, Timothy Baldwin, and Krister Lindén. Automatic Language Identification in Texts. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45822-4.

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Rao, K. Sreenivasa, and Dipanjan Nandi. Language Identification Using Excitation Source Features. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17725-0.

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Rao, K. Sreenivasa, V. Ramu Reddy, and Sudhamay Maity. Language Identification Using Spectral and Prosodic Features. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17163-0.

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United States. Bureau of the Census, ed. Language identification flashcard: 21st decennial census--1990. [Washington, D.C.?]: U.S. Dept. of Commerce, Bureau of the Census, 1990.

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Law, James, ed. The Early Identification of Language Impairment in Children. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-4445-0.

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Ethnic identification among urban Latinos: Language and flexibility. El Paso [Tex.]: LFB Scholarly Pub., 2011.

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1956-, Law James Christopher, ed. The Early identification of language impairment in children. London: Chapman & Hall, 1992.

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1925-, Adler Sol, ed. Early identification and intensive remediation of language retarded children. Springfield, Ill., U.S.A: Thomas, 1986.

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Keith, Lenz B., ed. The Word identification strategy. 2nd ed. Lawrence, Kan: University of Kansas, 1985.

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Book chapters on the topic "Language identification"

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Palakodety, Shriphani, Ashiqur R. KhudaBukhsh, and Guha Jayachandran. "Language Identification." In Low Resource Social Media Text Mining, 27–40. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5625-5_4.

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Gleason, H. A. "The Identification of Morphemes." In Language, 357–64. London: Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-13421-2_19.

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Hazen, Timothy J. "Topic Identification." In Spoken Language Understanding, 319–56. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119992691.ch12.

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de Jong-Lendle, Gea. "Speaker Identification." In Language as Evidence, 257–319. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84330-4_9.

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Fobbe, Eilika. "Authorship Identification." In Language as Evidence, 185–217. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84330-4_7.

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Jauhiainen, Tommi, Marcos Zampieri, Timothy Baldwin, and Krister Lindén. "Introduction to Language Identification." In Automatic Language Identification in Texts, 1–17. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45822-4_1.

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Schmidt, Anna Marie. "Cross-language consonant identification." In Language Experience in Second Language Speech Learning, 185–200. Amsterdam: John Benjamins Publishing Company, 2007. http://dx.doi.org/10.1075/lllt.17.18sch.

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Malmasi, Shervin, Iria del Río, and Marcos Zampieri. "Portuguese Native Language Identification." In Lecture Notes in Computer Science, 115–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99722-3_12.

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Winnemöller, Ronald. "Drive-by Language Identification." In Computational Linguistics and Intelligent Text Processing, 494–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12116-6_42.

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Baker, Joseph, Adam L. Kelly, Alexander B. T. McAuley, and Nick Wattie. "Language Games." In Talent Identification and Development in Youth Soccer, 316–26. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781032232799-20.

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Conference papers on the topic "Language identification"

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Farrell, Kevin R., Richard J. Mammone, and Allen Gorin. "Algebraic learning for language acquisition." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172523.

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Mabokela, Ronny. "Phone Clustering Methods for Multilingual Language Identification." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101421.

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This paper proposes phoneme clustering methods for multilingual language identification (LID) on a mixed-language corpus. A one-pass multilingual automated speech recognition (ASR) system converts spoken utterances into occurrences of phone sequences. Hidden Markov models were employed to train multilingual acoustic models that handle multiple languages within an utterance. Two phoneme clustering methods were explored to derive the most appropriate phoneme similarities between the target languages. Ultimately a supervised machine learning technique was employed to learn the language transition of the phonotactic information and engage the support vector machine (SVM) models to classify phoneme occurrences. The system performance was evaluated on mixed-language speech corpus for two South African languages (Sepedi and English) using the phone error rate (PER) and LID classification accuracy separately. We show that multilingual ASR which fed directly to the LID system has a direct impact on LID accuracy. Our proposed system has achieved an acceptable phone recognition and classification accuracy in mixed-language speech and monolingual speech (i.e. either Sepedi or English). Data-driven, and knowledge-driven phoneme clustering methods improve ASR and LID for code-switched speech. The data-driven method obtained the PER of 5.1% and LID classification accuracy of 94.5% when the acoustic models are trained with 64 Gaussian mixtures per state.

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Kargaran, Amir, Ayyoob Imani, François Yvon, and Hinrich Schuetze. "GlotLID: Language Identification for Low-Resource Languages." In Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-emnlp.410.

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Huang, Yichen, Hongyan Wang, Jeroen van de Weijer, and Zhengfei Zhu. "Native Language Identification." In ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377713.3377781.

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Remnev, N. V. "NATIVE LANGUAGE IDENTIFICATION FOR RUSSIAN USING ERRORS TYPES." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-1123-1133.

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The task of recognizing the author’s native (Native Language Identification—NLI) language based on a texts, written in a language that is non-native to the author—is the task of automatically recognizing native language (L1). The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 and 2017, where TOEFL English essays and essay samples were used as data. There is also a small number of works where the NLI problem was solved for other languages. The NLI problem was investigated for Russian by Ladygina (2017) and Remnev (2019). This paper discusses the use of well-established approaches in the NLI Shared Task 2013 and 2017 competitions to solve the problem of recognizing the author’s native language, as well as to recognize the type of speaker—learners of Russian or Heritage Russian speakers. Native language identification task is also solved based on the types of errors specific to different languages. This study is data-driven and is possible thanks to the Russian Learner Corpus developed by the Higher School of Economics (HSE) Learner Russian Research Group on the basis of which experiments are being conducted.

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Maier, Wolfgang, and Carlos Gómez-Rodríguez. "Language variety identification in Spanish tweets." In Proceedings of the EMNLP'2014 Workshop on Language Technology for Closely Related Languages and Language Variants. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-4204.

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Chakraborty, Joyshree, Priyankoo Sarmah, and Samudra Vijaya. "Spoken Language Identification of Four Tibeto-Burman languages." In 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA). IEEE, 2020. http://dx.doi.org/10.1109/o-cocosda50338.2020.9295008.

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Cavalin, Paulo, Pedro Domingues, Julio Nogima, and Claudio Pinhanez. "Understanding Native Language Identification for Brazilian Indigenous Languages." In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.americasnlp-1.3.

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Gris, Lucas Rafael Stefanel, and Arnaldo Candido Junior. "Automatic Spoken Language Identification using Convolutional Neural Networks." In Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/latinoware.2020.18603.

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Automatic Spoken Language Identification systems classify the spoken language automatically and can be used in many tasks, for example, to support Automatic Speech Recognition or Video Recommendation systems. In this work, we propose an automatic language identification model obtained through a Convolutional Neural Network trained over audio spectrograms on Portuguese, English and Spanish languages. The audio for the model training was obtained through audiobooks and different corpora for speech recognition systems. The audios were used to generate instances having five seconds each. We addressed the limitation of having few speakers in our dataset with simple data augmentation techniques such as speed and pitch changing on the original instances to increase the size of the dataset. The proposed model was optimized with a random hyperparameter search which provided a final model able to identify the proposed languages with 83% of accuracy on a new, unseen test data, made with audios from different sources.

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Van Dam, Juriaan Kennedy, and Vadim Zaytsev. "Software Language Identification with Natural Language Classifiers." In 2016 IEEE 23rd International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2016. http://dx.doi.org/10.1109/saner.2016.92.

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Reports on the topic "Language identification"

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Nowell, P., and D. A. Stevens. User Manual for Tactical Language Identification Software. Fort Belvoir, VA: Defense Technical Information Center, December 1997. http://dx.doi.org/10.21236/ada354329.

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Foged, Mette, and Cynthia van der Werf. Access to Language Training and the Local Integration of Refugees. Inter-American Development Bank, May 2023. http://dx.doi.org/10.18235/0004897.

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This paper examines whether language classes raises refugees' language proficiency and improves their socio-economic integration. Our identification strategy leverages the opening, closing, and gradual expansion of local language training centers in Denmark, as well as the quasi-random assignment of the refugees to locations with varying proximity to a language training center. First, we show that refugees' distance from the assigned language training center is as good as random conditional on initial placement. Second, we show that a one-hour decrease in commuting time increases total hours of class attended by 46 to 71. Third, we use this novel identification strategy to show that 100 additional hours of language class increases fluency in the Danish language by 8-9 percent, post-language training human capital acquisition by 11-13 percent and improve the integration of the refugees in the communities where they were initially placed, as measured by the lower exit rates from those same communities and an almost 70 percent reduction in mobility to the largest, most immigrant-dense cities in Denmark.

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Mendes, Diogo, Bruno Travassos, Adilson Marques, and Hugo Sarmento. Talent Identification and Development in Male Futsal: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0005.

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Review question / Objective: Identify and synthesize the most significant literature addressing talent identification and development in futsal. Condition being studied: Talent identification and development constraints associated to: (1) the athlete; (2) the environment; (3) the task. Eligibility criteria: The publications included in the first search round met the following criteria: (1) contained relevant data concerning talent identification and/or development; (2) were performed on male futsal players; (3) were empirical studies, and; (4) were written in the English, Spanish and Portuguese language. Studies were excluded if they: (1) included practitioners from other sports; (2) did not contain any relevant data on talent development and/or identification, and; (3) were reviews or conference proceedings.

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Luo, Xiaqin, and Luyao Chen. Speech and language features applied to the identification of patients with cognitive impairment: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0022.

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Bando, Rosangela, Florencia López Bóo, and Xia Li. Sex-Differences in Language and Socio-emotional Skills in Early Childhood. Inter-American Development Bank, July 2016. http://dx.doi.org/10.18235/0011759.

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This study explores sex differences in language and socio-emotional skills. It focuses on children 7 months old to 6 years old in Chile in 2012 and Nicaragua in 2013. A focus on young children allowed for ruling out a set of environmental and identity effects to explain the gap. Females had an advantage in both countries and both dimensions. Males in Chile scored at -0.13 standard deviations (SD) in language in the distribution of females. In addition, males scored at -0.20 SD in socio-emotional skills. The gaps in Nicaragua were not statistically different to those in Chile. Thus geographical and cultural variation across the two countries did not affect the gap. Within countries, variation in family characteristics, parenting practices and health investments did not explain the gap either. These findings shed light on the role of biological and environmental factors to explain sex gaps. The identification of the role of these factors is necessary to inform policy.

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Chong, Alberto E. Does It Matter How People Speak? Inter-American Development Bank, December 2006. http://dx.doi.org/10.18235/0010970.

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Language serves two key functions. It enables communication between agents, which allows for the establishment and operation of formal and informal institutions. It also serves a less obvious function, a reassuring quality more closely related to issues linked with trust, social capital, and cultural identification. While research on the role of language as a learning process is widespread, there is no evidence on the role of language as a signal of cultural affinity. I pursue this latter avenue of research and show that subtle language affinity is positively linked with change in earnings when using English-speaking data for cities in the Golden Horseshoe area in Southern Ontario during the period 1991 to 2001. The results are robust to changes in specification, a broad number of empirical tests, and a diverse set of outcome variables.

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Carlsson, Magnus, Stefan Eriksson, and Dan-Olof Rooth. Language Proficiency and Hiring of Immigrants: Evidence from a New Field Experimental Approach. Linnaeus University, School of Business and Economics, Department of Economics and Statistics., April 2023. http://dx.doi.org/10.15626/ns.wp.2023.1.

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Labor markets in advanced economies have undergone substantial change in recentdecades due to globalization, technological improvements, and organizational changes. Due tothese developments, oral and written language skills have become increasingly important evenin less skilled jobs. Immigrants – who often have limited skills in the host country languageupon arrival – are likely to be particularly affected by the increase in language requirements.Despite this increase in literacy requirements, little is known about how immigrants’ languageproficiency is rewarded in the labor market. However, estimating the causal effect ofimmigrants’ language skills on hiring is challenging due to potential biases caused by omittedvariables, reverse causality, and measurement error.To address identification problems, we conduct a large-scale field experiment, where wesend thousands of fictitious resumes to employers with a job opening. With the help of aprofessional linguist, we manipulate the cover letters by introducing common second-languagefeatures, which makes the resumes reflect variation in the language skills of real-worldmigrants. Our findings show that better language proficiency in the cover letter has a strongpositive effect on the callback rate for a job interview: moving from the lowest level of languageproficiency to a level similar to natives almost doubles the callback rate. Consistent with therecent development that language proficiency is also important for many low- and mediumskilledjobs, the effect of better language skills does not vary across the vastly different typesof occupations we study. Finally, the results from employer surveys suggest that it is improvedlanguage skills per se that is the dominant explanation behind the language proficiency effect,rather than language skills acting as a proxy for other unobserved abilities or characteristics.

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Pons, Aina, Annalisa Hauck, and Tarek Abdel Aziz. On Indocyanine Green Fluorescence and Autofluorescence in thyroid and parathyroid surgery: A systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0067.

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Review question / Objective: Autofluorescence (AF) and Indocyanine Green Fluorescence (ICG) were used for the first time for parathyroid gland (PG) identification in 2011 and 2015, respectively, during thyroidectomy/parathyroidectomy. Authors reported promising results. We aim to understand the efficacy, technical challenges, cost-effectiveness, and impact on postoperative biochemical and clinical outcomes of such new techniques. Eligibility criteria: The language filter was set to allow for publications in English, German, Spanish, and French assessing the use of ICG and/or AF for PG identification. Only titles and abstracts, followed by the full text dating from 2008 to 2020 have been considered in this review. Existing systematic reviews were excluded from the results.

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Rogers, Katherine, Karina Lovell, Peter Bower, and Christopher Armitage. “What are Deaf sign language users’ experiences as patients in healthcare services?”: A scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0102.

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Review question / Objective: A scoping review with specific reference to the context of Deaf populations, in relation to Deaf people’s experience of health and mental health services, including the use of a questionnaire regarding their experience as a patient, is needed in order to assess and synthesise the current knowledge. As this is an exploratory type of review drawing on qualitative as well as quantitative work, the PICo approach Population, (Phenomena of) Interest and Context, will guide the question formulation. Following the identification of the gap in the existing systematic reviews and scoping searches concerning patient experience and Deaf people’s experience of using healthcare services, the research question is as follows: “What are Deaf sign language users’ experiences as patients in healthcare services?”. Information sources: The bibliographic databases that will be searched for this review will includes PsycINFO, PubMed, Web of Science, CINAHL, and Medline. Grey literature sources (e.g., policy, practice, and guideline documents), including contacting the relevant investigators working in the field of Deaf populations, will be searched for this review study. Forward citation sources, from the relevant reference lists, will also be searched to ensure the process is thorough.

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Alvestrand, H. Tags for the Identification of Languages. RFC Editor, March 1995. http://dx.doi.org/10.17487/rfc1766.

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