Multi-Task Neural Model for Agglutinative Language Translation
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A Sentiment-Based Chat Bot
A sentiment-based chat bot Automatic Twitter replies with Python ALEXANDER BLOM SOFIE THORSEN Bachelor’s essay at CSC Supervisor: Anna Hjalmarsson Examiner: Mårten Björkman E-mail: [email protected], [email protected] Abstract Natural language processing is a field in computer science which involves making computers derive meaning from human language and input as a way of interacting with the real world. Broadly speaking, sentiment analysis is the act of determining the attitude of an author or speaker, with respect to a certain topic or the overall context and is an application of the natural language processing field. This essay discusses the implementation of a Twitter chat bot that uses natural language processing and sentiment analysis to construct a be- lievable reply. This is done in the Python programming language, using a statistical method called Naive Bayes classifying supplied by the NLTK Python package. The essay concludes that applying natural language processing and sentiment analysis in this isolated fashion was simple, but achieving more complex tasks greatly increases the difficulty. Referat Natural language processing är ett fält inom datavetenskap som innefat- tar att få datorer att förstå mänskligt språk och indata för att på så sätt kunna interagera med den riktiga världen. Sentiment analysis är, generellt sagt, akten av att försöka bestämma känslan hos en författare eller talare med avseende på ett specifikt ämne eller sammanhang och är en applicering av fältet natural language processing. Den här rapporten diskuterar implementeringen av en Twitter-chatbot som använder just natural language processing och sentiment analysis för att kunna svara på tweets genom att använda känslan i tweetet. -
Integrating Optical Character Recognition and Machine
Integrating Optical Character Recognition and Machine Translation of Historical Documents Haithem Afli and Andy Way ADAPT Centre School of Computing Dublin City University Dublin, Ireland haithem.afli, andy.way @adaptcentre.ie { } Abstract Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimenta- tion shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30% relative improvement. 1 Introduction While research on improving Optical Character Recognition (OCR) algorithms is ongoing, our assess- ment is that Machine Translation (MT) will continue to produce unacceptable translation errors (or non- translations) based solely on the automatic output of OCR systems. The problem comes from the fact that current OCR and Machine Translation systems are commercially distinct and separate technologies. There are often mistakes in the scanned texts as the OCR system occasionally misrecognizes letters and falsely identifies scanned text, leading to misspellings and linguistic errors in the output text (Niklas, 2010). Works involved in improving translation services purchase off-the-shelf OCR technology but have limited capability to adapt the OCR processing to improve overall machine translation perfor- mance. -
Applying Ensembling Methods to BERT to Boost Model Performance
Applying Ensembling Methods to BERT to Boost Model Performance Charlie Xu, Solomon Barth, Zoe Solis Department of Computer Science Stanford University cxu2, sbarth, zoesolis @stanford.edu Abstract Due to its wide range of applications and difficulty to solve, Machine Comprehen- sion has become a significant point of focus in AI research. Using the SQuAD 2.0 dataset as a metric, BERT models have been able to achieve superior performance to most other models. Ensembling BERT with other models has yielded even greater results, since these ensemble models are able to utilize the relative strengths and adjust for the relative weaknesses of each of the individual submodels. In this project, we present various ensemble models that leveraged BiDAF and multiple different BERT models to achieve superior performance. We also studied the effects how dataset manipulations and different ensembling methods can affect the performance of our models. Lastly, we attempted to develop the Synthetic Self- Training Question Answering model. In the end, our best performing multi-BERT ensemble obtained an EM score of 73.576 and an F1 score of 76.721. 1 Introduction Machine Comprehension (MC) is a complex task in NLP that aims to understand written language. Question Answering (QA) is one of the major tasks within MC, requiring a model to provide an answer, given a contextual text passage and question. It has a wide variety of applications, including search engines and voice assistants, making it a popular problem for NLP researchers. The Stanford Question Answering Dataset (SQuAD) is a very popular means for training, tuning, testing, and comparing question answering systems. -
Computational Challenges for Polysynthetic Languages
Computational Modeling of Polysynthetic Languages Judith L. Klavans, Ph.D. US Army Research Laboratory 2800 Powder Mill Road Adelphi, Maryland 20783 [email protected] [email protected] Abstract Given advances in computational linguistic analysis of complex languages using Machine Learning as well as standard Finite State Transducers, coupled with recent efforts in language revitalization, the time was right to organize a first workshop to bring together experts in language technology and linguists on the one hand with language practitioners and revitalization experts on the other. This one-day meeting provides a promising forum to discuss new research on polysynthetic languages in combination with the needs of linguistic communities where such languages are written and spoken. Finally, this overview article summarizes the papers to be presented, along with goals and purpose. Motivation Polysynthetic languages are characterized by words that are composed of multiple morphemes, often to the extent that one long word can express the meaning contained in a multi-word sentence in lan- guage like English. To illustrate, consider the following example from Inuktitut, one of the official languages of the Territory of Nunavut in Canada. The morpheme -tusaa- (shown in boldface below) is the root, and all the other morphemes are synthetically combined with it in one unit.1 (1) tusaa-tsia-runna-nngit-tu-alu-u-junga hear-well-be.able-NEG-DOER-very-BE-PART.1.S ‘I can't hear very well.’ Kabardian (Circassian), from the Northwest Caucasus, also shows this phenomenon, with the root -še- shown in boldface below: (2) wə-q’ə-d-ej-z-γe-še-ž’e-f-a-te-q’əm 2SG.OBJ-DIR-LOC-3SG.OBJ-1SG.SUBJ-CAUS-lead-COMPL-POTENTIAL-PAST-PRF-NEG ‘I would not let you bring him right back here.’ Polysynthetic languages are spoken all over the globe and are richly represented among Native North and South American families. -
Machine Translation for Academic Purposes Grace Hui-Chin Lin
Proceedings of the International Conference on TESOL and Translation 2009 December 2009: pp.133-148 Machine Translation for Academic Purposes Grace Hui-chin Lin PhD Texas A&M University College Station Master of Science, University of Southern California Paul Shih Chieh Chien Center for General Education, Taipei Medical University Abstract Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a valuable approach to apply by professional translators for diverse professional fields. For learning translating skills and finding a way to learn and teach through bi-lingual/multilingual translating functions in software, machine translation is an ideal approach that translation trainers, translation learners, and professional translators should be familiar with. In fact, theories for machine translation and computer assistance had been highly valued by many scholars. (e.g., Hutchines, 2003; Thriveni, 2002) Based on MIT’s Open Courseware into Chinese that Lee, Lin and Bonk (2007) have introduced, this paper demonstrates how MT can be efficiently applied as a superior way of teaching and learning. This article predicts the translated courses utilizing MT for residents of global village should emerge and be provided soon in industrialized nations and it exhibits an overview about what the current developmental status of MT is, why the MT should be fully applied for academic purpose, such as translating a textbook or teaching and learning a course, and what types of software can be successfully applied. -
Aconcorde: Towards a Proper Concordance for Arabic
aConCorde: towards a proper concordance for Arabic Andrew Roberts, Dr Latifa Al-Sulaiti and Eric Atwell School of Computing University of Leeds LS2 9JT United Kingdom {andyr,latifa,eric}@comp.leeds.ac.uk July 12, 2005 Abstract Arabic corpus linguistics is currently enjoying a surge in activity. As the growth in the number of available Arabic corpora continues, there is an increased need for robust tools that can process this data, whether it be for research or teaching. One such tool that is useful for both groups is the concordancer — a simple tool for displaying a specified target word in its context. However, obtaining one that can reliably cope with the Arabic lan- guage had proved extremely difficult. Therefore, aConCorde was created to provide such a tool to the community. 1 Introduction A concordancer is a simple tool for summarising the contents of corpora based on words of interest to the user. Otherwise, manually navigating through (of- ten very large) corpora would be a long and tedious task. Concordancers are therefore extremely useful as a time-saving tool, but also much more. By isolat- ing keywords in their contexts, linguists can use concordance output to under- stand the behaviour of interesting words. Lexicographers can use the evidence to find if a word has multiple senses, and also towards defining their meaning. There is also much research and discussion about how concordance tools can be beneficial for data-driven language learning (Johns, 1990). A number of studies have demonstrated that providing access to corpora and concordancers benefited students learning a second language. -
Machine Translation for Language Preservation
Machine translation for language preservation Steven BIRD1,2 David CHIANG3 (1) Department of Computing and Information Systems, University of Melbourne (2) Linguistic Data Consortium, University of Pennsylvania (3) Information Sciences Institute, University of Southern California [email protected], [email protected] ABSTRACT Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of document- ing the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected in documentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands of Papua New Guinea. KEYWORDS: endangered languages, documentary linguistics, language resources, bilingual texts, comparative lexicons. Proceedings of COLING 2012: Posters, pages 125–134, COLING 2012, Mumbai, December 2012. 125 1 Introduction Most of the world’s 6800 languages are relatively unstudied, even though they are no less im- portant for scientific investigation than major world languages. For example, before Hixkaryana (Carib, Brazil) was discovered to have object-verb-subject word order, it was assumed that this word order was not possible in a human language, and that some principle of universal grammar must exist to account for this systematic gap (Derbyshire, 1977). -
“Today's Morphology Is Yeatyerday's Syntax”
MOTHER TONGUE Journal of the Association for the Study of Language in Prehistory • Issue XXI • 2016 From North to North West: How North-West Caucasian Evolved from North Caucasian Viacheslav A. Chirikba Abkhaz State University, Sukhum, Republic of Abkhazia The comparison of (North-)West Caucasian with (North-)East Caucasian languages suggests that early Proto-West Caucasian underwent a fundamental reshaping of its phonological, morphological and syntactic structures, as a result of which it became analytical, with elementary inflection and main grammatical roles being expressed by lexical means, word order and probably also by tones. The subsequent development of compounding and incorporation resulted in a prefixing polypersonal polysynthetic agglutinative language type typical for modern West Caucasian languages. The main evolutionary line from a North Caucasian dialect close to East Caucasian to modern West Caucasian languages was thus from agglutinative to the analytical language-type, due to a near complete loss of inflection, and then to the agglutinative polysynthetic type. Although these changes blurred the genetic relationship between West Caucasian and East Caucasian languages, however, this can be proven by applying standard procedures of comparative-historical linguistics. 1. The West Caucasian languages.1 The West Caucasian (WC), or Abkhazo-Adyghean languages constitute a branch of the North Caucasian (NC) linguistic family, which consists of five languages: Abkhaz and Abaza (the Abkhaz sub-group), Adyghe and Kabardian (the Circassian sub-group), and Ubykh. The traditional habitat of these languages is the Western Caucasus, where they are still spoken, with the exception of the extinct Ubykh. Typologically, the WC languages represent a rather idiosyncratic linguistic type not occurring elsewhere in Eurasia. -
Hunspell – the Free Spelling Checker
Hunspell – The free spelling checker About Hunspell Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex word compounding or character encoding. Hunspell interfaces: Ispell-like terminal interface using Curses library, Ispell pipe interface, OpenOffice.org UNO module. Main features of Hunspell spell checker and morphological analyzer: - Unicode support (affix rules work only with the first 65535 Unicode characters) - Morphological analysis (in custom item and arrangement style) and stemming - Max. 65535 affix classes and twofold affix stripping (for agglutinative languages, like Azeri, Basque, Estonian, Finnish, Hungarian, Turkish, etc.) - Support complex compoundings (for example, Hungarian and German) - Support language specific features (for example, special casing of Azeri and Turkish dotted i, or German sharp s) - Handle conditional affixes, circumfixes, fogemorphemes, forbidden words, pseudoroots and homonyms. - Free software (LGPL, GPL, MPL tri-license) Usage The src/tools dictionary contains ten executables after compiling (or some of them are in the src/win_api): affixcompress: dictionary generation from large (millions of words) vocabularies analyze: example of spell checking, stemming and morphological analysis chmorph: example of automatic morphological generation and conversion example: example of spell checking and suggestion hunspell: main program for spell checking and others (see manual) hunzip: decompressor of hzip format hzip: compressor of -
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-Mail: Olivia [email protected]
Machine Translation and Computer-Assisted Translation: a New Way of Translating? Author: Olivia Craciunescu E-mail: [email protected] Author: Constanza Gerding-Salas E-mail: [email protected] Author: Susan Stringer-O’Keeffe E-mail: [email protected] Source: http://www.translationdirectory.com The Need for Translation IT has produced a screen culture that Technology tends to replace the print culture, with printed documents being dispensed Advances in information technology (IT) with and information being accessed have combined with modern communication and relayed directly through computers requirements to foster translation automation. (e-mail, databases and other stored The history of the relationship between information). These computer documents technology and translation goes back to are instantly available and can be opened the beginnings of the Cold War, as in the and processed with far greater fl exibility 1950s competition between the United than printed matter, with the result that the States and the Soviet Union was so intensive status of information itself has changed, at every level that thousands of documents becoming either temporary or permanent were translated from Russian to English and according to need. Over the last two vice versa. However, such high demand decades we have witnessed the enormous revealed the ineffi ciency of the translation growth of information technology with process, above all in specialized areas of the accompanying advantages of speed, knowledge, increasing interest in the idea visual -
Language Service Translation SAVER Technote
TechNote August 2020 LANGUAGE TRANSLATION APPLICATIONS The U.S. Department of Homeland Security (DHS) AEL Number: 09ME-07-TRAN established the System Assessment and Validation for Language translation equipment used by emergency services has evolved into Emergency Responders commercially available, mobile device applications (apps). The wide adoption of (SAVER) Program to help cell phones has enabled language translation applications to be useful in emergency responders emergency scenarios where first responders may interact with individuals who improve their procurement speak a different language. Most language translation applications can identify decisions. a spoken language and translate in real-time. Applications vary in functionality, Located within the Science such as the ability to translate voice or text offline and are unique in the number and Technology Directorate, of available translatable languages. These applications are useful tools to the National Urban Security remedy communication barriers in any emergency response scenario. Technology Laboratory (NUSTL) manages the SAVER Overview Program and conducts objective operational During an emergency response, language barriers can challenge a first assessments of commercial responder’s ability to quickly assess and respond to a situation. First responders equipment and systems must be able to accurately communicate with persons at an emergency scene in relevant to the emergency order to provide a prompt, appropriate response. The quick translations provided responder community. by mobile apps remove the language barrier and are essential to the safety of The SAVER Program gathers both first responders and civilians with whom they interact. and reports information about equipment that falls within the Before the adoption of smartphones, first responders used language interpreters categories listed in the DHS and later language translation equipment such as hand-held language Authorized Equipment List translation devices (Figure 1). -
Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, Serge Sharoff
TTC: Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, Serge Sharoff To cite this version: Helena Blancafort, Béatrice Daille, Tatiana Gornostay, Ulrich Heid, Claude Méchoulam, et al.. TTC: Terminology Extraction, Translation Tools and Comparable Corpora. 14th EURALEX International Congress, Jul 2010, Leeuwarden/Ljouwert, Netherlands. pp.263-268. hal-00819365 HAL Id: hal-00819365 https://hal.archives-ouvertes.fr/hal-00819365 Submitted on 2 May 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. TTC: Terminology Extraction, Translation Tools and Comparable Corpora Helena Blancafort, Syllabs, Universitat Pompeu Fabra, Barcelona, Spain Béatrice Daille, LINA, Université de Nantes, France Tatiana Gornostay, Tilde, Latva Ulrich Heid, IMS, Universität Stuttgart, Germany Claude Mechoulam, Sogitec, France Serge Sharoff, CTS, University of Leeds, England The need for linguistic resources in any natural language application is undeniable. Lexicons and terminologies