Search Results for "human"
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Showing 8 open source projects for "human"
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Tokenized Text Aligner
Aligns tokens in two versions of a text with differing tokenization.
...It is intended for use in the preparation of annotated linguistic corpora, where differences in tokenization may arise (i) following corrections or modifications to the source text or (ii) through the creation of different layers of annotation (part-of-speech, treebank) requiring different tokenization. In its default implementation, it produces a human-readable CSV table associating tokens in text A with tokens in text B, and can also inject token-level annotation from text B to text A. The Aligner class on which the default implementation is based can be incorporated into more complex workflows. -
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MITRE Annotation Toolkit
A toolkit for managing and manipulating text annotations
The MITRE Annotation Toolkit (MAT) is a suite of tools which can be used for automated and human tagging of annotations. Annotation is a process, used mostly by researchers in natural language processing, of enhancing documents with information about the various phrase types the documents contain. MAT supports both UI interaction and command-line interaction, and provides various levels of control over the overall annotation process. -
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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. ... -
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Phrasal
Statistical phrase-based machine translation system
...Developed by The Natural Language Processing Group at Stanford University, a team of faculty, postdocs, programmers and students who work together on algorithms that allow computers to process and understand human languages. Our work ranges from basic research in computational linguistics to key applications in human language technology, and covers areas such as sentence understanding, automatic question answering, machine translation, syntactic parsing and tagging, sentiment analysis. -
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This project concerns the development of human language technology resources, based on the approach to share or recycle resources between closely related language. http://gerhard.pro/closely-related-languages/
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eNTranslator
To aid translation of satsangs of Paramhamsa Nithyananda
...Can be used for general purpose by others as well. This translator desktop app uses google translator to translate English text. The auto generated translations are then enriched with human alternation using an easy graphical user interface. Time stamp information may be synched and a subtitle file or a simple textual output may be generated. Additionally it is planned to use google voice tools to also add voice over from these translated text. Finally the subtitle, translated audio (if any) would be muxed with the original video and uploaded. -
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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... -
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Mechaglot, Calculate Semantic Similarity
Calculate semantic similarity for any human and human-like languages
WARNING: There are too many false-positives! This is Alpha release, expect many things to improve, including the algorithms. PLEASE GO TO BROWSE ALL FILES TO READ A FULL DESCRIPTION. The goal of this project is simple: Input two sentences of the same language, and obtain the number (from 0 to 1) denoting the similarity between the inputted sentences, according to semantic categories. This project models my previous...
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