Aconcorde: Towards a Proper Concordance for Arabic
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Preparation and Exploitation of Bilingual Texts Dusko Vitas, Cvetana Krstev, Eric Laporte
Preparation and exploitation of bilingual texts Dusko Vitas, Cvetana Krstev, Eric Laporte To cite this version: Dusko Vitas, Cvetana Krstev, Eric Laporte. Preparation and exploitation of bilingual texts. Lux Coreana, 2006, 1, pp.110-132. hal-00190958v2 HAL Id: hal-00190958 https://hal.archives-ouvertes.fr/hal-00190958v2 Submitted on 27 Nov 2007 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. Preparation and exploitation of bilingual texts Duško Vitas Faculty of Mathematics Studentski trg 16, CS-11000 Belgrade, Serbia Cvetana Krstev Faculty of Philology Studentski trg 3, CS-11000 Belgrade, Serbia Éric Laporte Institut Gaspard-Monge, Université de Marne-la-Vallée 5, bd Descartes, 77454 Marne-la-Vallée CEDEX 2, France Introduction A bitext is a merged document composed of two versions of a given text, usually in two different languages. An aligned bitext is produced by an alignment tool or aligner, that automatically aligns or matches the versions of the same text, generally sentence by sentence. A multilingual aligned corpus or collection of aligned bitexts, when consulted with a search tool, can be extremely useful for translation, language teaching and the investigation of literary text (Veronis, 2000). -
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. -
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. -
The Iafor European Conference Series 2014 Ece2014 Ecll2014 Ectc2014 Official Conference Proceedings ISSN: 2188-1138
the iafor european conference series 2014 ece2014 ecll2014 ectc2014 Official Conference Proceedings ISSN: 2188-1138 “To Open Minds, To Educate Intelligence, To Inform Decisions” The International Academic Forum provides new perspectives to the thought-leaders and decision-makers of today and tomorrow by offering constructive environments for dialogue and interchange at the intersections of nation, culture, and discipline. Headquartered in Nagoya, Japan, and registered as a Non-Profit Organization 一般社( 団法人) , IAFOR is an independent think tank committed to the deeper understanding of contemporary geo-political transformation, particularly in the Asia Pacific Region. INTERNATIONAL INTERCULTURAL INTERDISCIPLINARY iafor The Executive Council of the International Advisory Board IAB Chair: Professor Stuart D.B. Picken IAB Vice-Chair: Professor Jerry Platt Mr Mitsumasa Aoyama Professor June Henton Professor Frank S. Ravitch Director, The Yufuku Gallery, Tokyo, Japan Dean, College of Human Sciences, Auburn University, Professor of Law & Walter H. Stowers Chair in Law USA and Religion, Michigan State University College of Law Professor David N Aspin Professor Emeritus and Former Dean of the Faculty of Professor Michael Hudson Professor Richard Roth Education, Monash University, Australia President of The Institute for the Study of Long-Term Senior Associate Dean, Medill School of Journalism, Visiting Fellow, St Edmund’s College, Cambridge Economic Trends (ISLET) Northwestern University, Qatar University, UK Distinguished Research Professor of Economics, -
Kernerman Kdictionaries.Com/Kdn DICTIONARY News the European Network of E-Lexicography (Enel) Tanneke Schoonheim
Number 22 ● July 2014 Kernerman kdictionaries.com/kdn DICTIONARY News The European Network of e-Lexicography (ENeL) Tanneke Schoonheim On October 11th 2013, the kick-off meeting of the European production and reception of dictionaries. The internet offers Network of e-Lexicography (ENeL) project took place in entirely new possibilities for developing and presenting Brussels. This meeting was the outcome of an idea ventilated dictionary information, such as with the integration of sound, a year and a half earlier, in March 2012 in Berlin, at the maps or video, and various novel ways of interacting with European Workshop on Future Standards in Lexicography. dictionary users. For editors of scholarly dictionaries the new The workshop participants then confirmed the imperative to medium is not only a source of inspiration, it also generates coordinate and harmonise research in the field of (electronic) new and serious challenges that demand cooperation and lexicography across Europe, namely to share expertise relating standardization on various levels: to standards, discuss new methodologies in lexicography that a. Through the internet scholarly dictionaries can potentially fully exploit the possibilities of the digital medium, reflect on reach large audiences. However, at present scholarly the pan-European nature of the languages of Europe and attain dictionaries providing reliable information are often not easy a wider audience. to find and are hard to decode for a non-academic audience; A proposal was written by a team of researchers from -
TANGO: Bilingual Collocational Concordancer
TANGO: Bilingual Collocational Concordancer Jia-Yan Jian Yu-Chia Chang Jason S. Chang Department of Computer Inst. of Information Department of Computer Science System and Applictaion Science National Tsing Hua National Tsing Hua National Tsing Hua University University University 101, Kuangfu Road, 101, Kuangfu Road, 101, Kuangfu Road, Hsinchu, Taiwan Hsinchu, Taiwan Hsinchu, Taiwan [email protected] [email protected] [email protected] du.tw on elaborated statistical calculation. Moreover, log Abstract likelihood ratios are regarded as a more effective In this paper, we describe TANGO as a method to identify collocations especially when the collocational concordancer for looking up occurrence count is very low (Dunning, 1993). collocations. The system was designed to Smadja’s XTRACT is the pioneering work on answer user’s query of bilingual collocational extracting collocation types. XTRACT employed usage for nouns, verbs and adjectives. We first three different statistical measures related to how obtained collocations from the large associated a pair to be collocation type. It is monolingual British National Corpus (BNC). complicated to set different thresholds for each Subsequently, we identified collocation statistical measure. We decided to research and instances and translation counterparts in the develop a new and simple method to extract bilingual corpus such as Sinorama Parallel monolingual collocations. Corpus (SPC) by exploiting the word- We also provide a web-based user interface alignment technique. The main goal of the capable of searching those collocations and its concordancer is to provide the user with a usage. The concordancer supports language reference tools for correct collocation use so learners to acquire the usage of collocation. -
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 -
Package 'Ptstem'
Package ‘ptstem’ January 2, 2019 Type Package Title Stemming Algorithms for the Portuguese Language Version 0.0.4 Description Wraps a collection of stemming algorithms for the Portuguese Language. URL https://github.com/dfalbel/ptstem License MIT + file LICENSE LazyData TRUE RoxygenNote 5.0.1 Encoding UTF-8 Imports dplyr, hunspell, magrittr, rslp, SnowballC, stringr, tidyr, tokenizers Suggests testthat, covr, plyr, knitr, rmarkdown VignetteBuilder knitr NeedsCompilation no Author Daniel Falbel [aut, cre] Maintainer Daniel Falbel <[email protected]> Repository CRAN Date/Publication 2019-01-02 14:40:02 UTC R topics documented: complete_stems . .2 extract_words . .2 overstemming_index . .3 performance . .3 ptstem_words . .4 stem_hunspell . .5 stem_modified_hunspell . .5 stem_porter . .6 1 2 extract_words stem_rslp . .7 understemming_index . .7 unify_stems . .8 Index 9 complete_stems Complete Stems Description Complete Stems Usage complete_stems(words, stems) Arguments words character vector of words stems character vector of stems extract_words Extract words Extracts all words from a character string of texts. Description Extract words Extracts all words from a character string of texts. Usage extract_words(texts) Arguments texts character vector of texts Note it uses the regex \b[:alpha:]+\b to extract words. overstemming_index 3 overstemming_index Overstemming Index (OI) Description It calculates the proportion of unrelated words that were combined. Usage overstemming_index(words, stems) Arguments words is a data.frame containing a column word a a column group so the function can identify groups of words. stems is a character vector with the stemming result for each word performance Performance Description Performance Usage performance(stemmers = c("rslp", "hunspell", "porter", "modified-hunspell")) Arguments stemmers a character vector with names of stemming algorithms. -
Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task
Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task Rahmad Mahendra, Heninggar Septiantri, Haryo Akbarianto Wibowo, Ruli Manurung, and Mirna Adriani Faculty of Computer Science, Universitas Indonesia Depok 16424, West Java, Indonesia [email protected], fheninggar, [email protected] Abstract after the target word), the nearest verb from the target word, the transitive verb around the target Ambiguity is a problem we frequently face word, and the document context. Unfortunately, in Natural Language Processing. Word neither the model nor the corpus from this research Sense Disambiguation (WSD) is a task to is made publicly available. determine the correct sense of an ambigu- This paper reports our study on WSD task for ous word. However, research in WSD for Indonesian using the combination of the cross- Indonesian is still rare to find. The avail- lingual and supervised learning approach. Train- ability of English-Indonesian parallel cor- ing data is automatically acquired using Cross- pora and WordNet for both languages can Lingual WSD (CLWSD) approach by utilizing be used as training data for WSD by ap- WordNet and parallel corpus. Then, the monolin- plying Cross-Lingual WSD method. This gual WSD model is built from training data and training data is used as an input to build it is used to assign the correct sense to any previ- a model using supervised machine learn- ously unseen word in a new context. ing algorithms. Our research also exam- ines the use of Word Embedding features 2 Related Work to build the WSD model. WSD task is undertaken in two main steps, namely 1 Introduction listing all possible senses for a word and deter- mining the right sense given its context (Ide and One of the biggest challenges in Natural Language Veronis,´ 1998). -
A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
information Article A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection † Hanqing Zhou *, Amal Zouaq and Diana Inkpen School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada; [email protected] (A.Z.); [email protected] (D.I.) * Correspondence: [email protected]; Tel.: +1-613-562-5800 † This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322. Received: 6 November 2018; Accepted: 20 December 2018; Published: 25 December 2018 Abstract: This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning 1. Introduction The Semantic Web is defined by Berners-Lee et al. -
Panacea D6.1
SEVENTH FRAMEWORK PROGRAMME THEME 3 Information and communication Technologies PANACEA Project Grant Agreement no.: 248064 Platform for Automatic, Normalized Annotation and Cost-Effective Acquisition of Language Resources for Human Language Technologies D6.1 Technologies and Tools for Lexical Acquisition Dissemination Level: Public Delivery Date: July 16th 2010 Status – Version: Final Author(s) and Affiliation: Laura Rimell (UCAM), Anna Korhonen (UCAM), Valeria Quochi (ILC-CNR), Núria Bel (UPF), Tommaso Caselli (ILC-CNR), Prokopis Prokopidis (ILSP), Maria Gavrilidou (ILSP), Thierry Poibeau (UCAM), Muntsa Padró (UPF), Eva Revilla (UPF), Monica Monachini(CNR-ILC), Maurizio Tesconi (CNR-IIT), Matteo Abrate (CNR-IIT) and Clara Bacciu (CNR-IIT) D6.1 Technologies and Tools for Lexical Acquisition This document is part of technical documentation generated in the PANACEA Project, Platform for Automatic, Normalized Annotation and Cost-Effective Acquisition (Grant Agreement no. 248064). This documented is licensed under a Creative Commons Attribution 3.0 Spain License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/es/. Please send feedback and questions on this document to: [email protected] TRL Group (Tecnologies dels Recursos Lingüístics), Institut Universitari de Lingüística Aplicada, Universitat Pompeu Fabra (IULA-UPF) D6.1 – Technologies and Tools for Lexical Acquisition Table of contents Table of contents .......................................................................................................................... -
Contextual Word Embedding for Multi-Label Classification Task Of
Contextual word embedding for multi-label classification task of scientific articles Yi Ning Liang University of Amsterdam 12076031 ABSTRACT sense of bank is ambiguous and it shares the same representation In Natural Language Processing (NLP), using word embeddings as regardless of the context it appears in. input for training classifiers has become more common over the In order to deal with the ambiguity of words, several methods past decade. Training on a lager corpus and more complex models have been proposed to model the individual meaning of words and has become easier with the progress of machine learning tech- an emerging branch of study has focused on directly integrating niques. However, these robust techniques do have their limitations the unsupervised embeddings into downstream applications. These while modeling for ambiguous lexical meaning. In order to capture methods are known as contextualized word embedding. Most of the multiple meanings of words, several contextual word embeddings prominent contextual representation models implement a bidirec- methods have emerged and shown significant improvements on tional language model (biLM) that concatenates the output vector a wide rage of downstream tasks such as question answering, ma- of the left-to-right output vector and right-to-left using a LSTM chine translation, text classification and named entity recognition. model [2–5] or transformer model [6, 7]. This study focuses on the comparison of classical models which use The contextualized word embeddings have achieved state-of-the- static representations and contextual embeddings which implement art results on many tasks from the major NLP benchmarks with the dynamic representations by evaluating their performance on multi- integration of the downstream task.