Open Source Linux Linguistics Software
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Linguistics Software for Linux
View 270 business solutionsBrowse free open source Linguistics software and projects for Linux below. Use the toggles on the left to filter open source Linguistics software by OS, license, language, programming language, and project status.
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sgmweka
Weka wrapper for the SGM toolkit for text classification and modeling.
Weka wrapper for the SGM toolkit for text classification and modeling. Provides Sparse Generative Models for scalable and accurate text classification and modeling for use in high-speed and large-scale text mining. Has lower time complexity of classification than comparable software due to inference based on sparse model representation and use of an inverted index. The provided .zip file is in the Weka package format, giving access to text classification. Other functions are usable through either Java command-line commands or class inclusion into Java projects. -
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PADIC
A multilingual Parallel Arabic DIalectal Corpus
PADIC (Parallel Arabic DIalectal Corpus) is a multi-dialectal corpus built in the framework of the National Research Project "TORJMAN", led by Scientific and Technical Research Center for the Development of Arabic Language and funded by the Algerian Ministry of Higher Education and Scientific Research. PADIC is composed of 6 dialects: two Algerian dialects (Algiers and Annaba cities), Palestinian, Syrian, Tunisian, Moroccan) and MSA. Mourad Abbas Computational Linguistics Department, crstdla https://sites.google.com/site/mouradabbas9 Publications ----------------- K. Meftouh, S. Harrat, S. Jamoussi, M. Abbas, K. Smaïli, Machine Translation Experiments on PADIC: A Parallel Arabic DIalect Corpus, The 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015, Shanghai, 2015. TORJMAN website: ------------------------- https://sites.google.com/site/torjmanepnr/6-corpus -
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Arabic Corpus
Text categorization, arabic language processing, language modeling
The Arabic Corpus {compiled by Dr. Mourad Abbas ( http://sites.google.com/site/mouradabbas9/corpora ) The corpus Khaleej-2004 contains 5690 documents. It is divided to 4 topics (categories). The corpus Watan-2004 contains 20291 documents organized in 6 topics (categories). Researchers who use these two corpora would mention the two main references: (1) For Watan-2004 corpus ---------------------- M. Abbas, K. Smaili, D. Berkani, (2011) Evaluation of Topic Identification Methods on Arabic Corpora,JOURNAL OF DIGITAL INFORMATION MANAGEMENT,vol. 9, N. 5, pp.185-192. 2) For Khaleej-2004 corpus --------------------------------- M. Abbas, K. Smaili (2005) Comparison of Topic Identification Methods for Arabic Language, RANLP05 : Recent Advances in Natural Language Processing ,pp. 14-17, 21-23 september 2005, Borovets, Bulgary. More useful references to check: ------------------------------------------- https://sites.google.com/site/mouradabbas9/corpora -
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Maui is a multi-purpose automatic topic indexing algorithm. Given a document, Maui automatically identifies its topics. Depending on the task topics are tags, keywords, keyphrases, vocabulary terms, descriptors or Wikipedia titles.
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Sanchay is a collection of tools and APIs for language researchers. It has some implementations of NLP algorithms, some flexible APIs, several user friendly annotation interfaces and Sanchay Query Language for language resources.
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The AK toolkit is another kit for building and use Hidden Markov Models (HMMs). Originally developed for handwritten text recognition (HTR) using Bernoulli HMMs, it also implements diagonal Gaussians and can be used for any other purpose.
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ANT is a lightweight implementation in C of a kind of artificial neural net called Multilayer Perceptron, which uses the backpropagation algorithm as learning method. The package includes an introductory example to start using artificial neural nets.
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AraRooter
Find Arabic Root Word
Using Machine Learning, AraRooter finds the three-lettered root of any Arabic lemma with around 84% accuracy. -
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ArabicDiacritizer
An automatic restoration of Arabic diacritic marks
This is a software of Arabic diacritical marks restoration. It is based mainly on deep architectures using deep neural network. The algorithm generates diacritized text with determined end case. The algorithm is described in detail in: Ilyes Rebai, and Yassine BenAyed 'Text-to-speech synthesis system with Arabic diacritic recognition system', Computer Speech & Language, 2015. We appreciate it very much if you can cite our related work. ************** Installation *************** - Extract the archive "ArabicDiacritizer Setup.rar". - Install the application using "Setup.exe". - Put an Arabic text in the Text Box. - Start the diacritization process. If the following problem occured: <Access to the path '..\ArabicDiacritizer v1.0\text.data' is denied> - Access to the path "Program Files\ArabicDiacritizer\ArabicDiacritizer v1.0\", - Right click on "ArabicDiacritizer" - Choose "Run as administrator" For further information, please contact: rebai_ily -
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Bermuda Text-to-Speech
This project includes basic NLP and DSP techniques for Text-to-Speech
See TTS demo at: http://rslp.racai.ro/index.php?page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro. -
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CRFSharp
CRFSharp is a .NET(C#) implementation of Conditional Random Field
CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly. -
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Drug Extraction
Drug name extraction
Drug name recognition and normalisation/grounding to DrugBank ids and standard names. Package provides 2 taggers: 1. DrugTagger - CRF-based with DrugBank presence feature (see feature set for details). 2. DrugnameGazetteer - gazetteer/dictionary-based. Dictionary created from DrugBank.ca database. Both taggers include grounding/normalisation to DrugBank ids and standard names. Feature set: Word, Word-1, Word+1, Word-1_Word, Word_Word+1, DrugBankPresence, POS DrugBankPresence feature indicates the presence of the drug name in the DrugBank. Using CONLL-Evaluation: processed 32065 tokens with 3656 phrases; found: 3251 phrases; correct: 2786. accuracy: 95.25%; precision: 85.70%; recall: 76.20%; FB1: 80.67 Using GATE Corpus Benchmark: Strict: P: 0.65 R: 0.73 F1: 0.69 Lenient: P: 0.74 R: 0.84 F1: 0.78 The details of how to reproduce evaluation, see README. To use standalone version for tagging download DrugExtractionStandalone.tar.gz from Files. -
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The goal of this project is to investigate optimal ways to do genre classification for the ten indigenous South African languages. Funded by Dept of Arts and Culture of the SA Government. http://www.trifonius.co.za/projects/genre-classification
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LexSub
A Lexical Substitution Framework
Lexical substitution framework for supervised all-words lexical substitution using delexicalized features. For a runnable (but GPL-licensed) version of LexSub, see LexSub-GPL (sf.net/p/lexsub/lexsub-gpl) -
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MTBook
Machine Translation: Foundations and Models
This is a tutorial, the purpose is to introduce the basic knowledge and modeling methods of machine translation systematically, and on this basis, discuss some cutting-edge technologies of machine translation (formerly known as "Machine Translation: Statistical Modeling and Deep Learning") method"). Its content is compiled into a book, which can be used for the study of senior undergraduates and graduate students in computer and artificial intelligence related majors, and can also be used as reference material for researchers related to natural language processing, especially machine translation. This book is written in tex, and all source codes are open. This book is divided into four parts, each of which consists of several chapters. The order of the chapters refers to the time context of the development of machine translation technology, while taking into account the internal logic of the machine translation knowledge system. -
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Speakable Programming for Every Language
Your language to speak with all.
This project has the language data for spel, the main new codebase is at: https://gitlab.com/liberit/pyac A computer programming language using human language syntax for human-to-human and human-to-computer communication with high precision, supporting many languages. Currently has alpha prototype support for analytic versions of the UN languages English, Mandarin Chinese, Spanish, Arabic, Russian and French as well as a bunch of others in addition to the core mwak language. The alpha IDE is at http://spel.sourceforge.net/src/web/spel.html (wait for it to finish loading before clicking "translate") Since it is early prototype, it's not easy to use, If you are interested, join the mailing list. latest code is in the git repository. -
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Supertagger
Software for assigning supertags.
Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree. -
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SweetOnionCCG2PTBConverter
A tool that converts CCGBank to PTB
Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%. -
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TEES
Turku Event Extraction System
Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information. -
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Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.
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Unitag is a language-independent Unicode-based part-of-speech tagging system. Written entirely in ANSI-compatible C, it should (in theory) compile on any OS, but has been tested on 32-bit Windows.
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iGREAT is an open-source, statistical machine translation software toolkit based on finite-state models.
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jaf_Kernels
Similarity Word-Sequence Kernels for Sentence Clustering toolkit
This project implements the techniques used in this paper: @INPROCEEDINGS{Andres10a, author = {Jesús Andrés-Ferrer and Germán Sanchis-Trilles and Francisco Casacuberta}, title = {Similarity Word-Sequence Kernels for Sentence Clustering}, booktitle = {Proceedings of the 8th International Workshop on Statistical Pattern Recognition}, year = {2010}, } This project depends on jaf_Utils: http://sourceforge.net/projects/jafutils/ Install it prior installation of jaf_Kernels. -
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jaf_MT
This implements a phrased-based hidden semi-Markov Model for SMT
This package implements the phrased-based hidden semi-Markov model described: Jesús Andrés-Ferrer, Alfons Juan. A phrase-based hidden semi-Markov approach to machine translation. Procedings of European Association for Machine Translation (EAMT), 2009. pp. 168-175. This project depends on jaf_Utils: http://sourceforge.net/projects/jafutils/ Install it prior installation of jaf_MT.
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