Open Source Linux Text Processing Software

Language is complicated and there's a gazillion words. Compromise is a javascript library that interprets and pre-parses text and makes some reasonable decisions so things are way easier. Compromise tries its best to parse text. it is small, quick, and often good-enough. It is not as smart as you'd think. Conjugate and negate verbs in any tense. Play between plural, singular and possessive forms. Interpret plain-text numbers. Handle implicit terms. Use it on the client-side or as an es-module. compromise is 180kb (minified). It's pretty fast. It can run on keypress. It works mainly by conjugating all forms of a basic word list. Decide how words get interpreted or make heavier changes with a compromise-plugin. Parse text without running POS-tagging. Pre-parse any match statements for faster lookups. It is not the most accurate, or clever nlp library, but found its niche as an easy, small library that can run everywhere.

FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ext classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool. The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish. The centerpiece of CoreNLP is the pipeline. Pipelines take in raw text, run a series of NLP annotators on the text, and produce a final set of annotations. Pipelines produce CoreDocuments, data objects that contain all of the annotation information, accessible with a simple API, and serializable to a Google Protocol Buffer. CoreNLP generates a variety of linguistic annotations, including parts of speech, named entities, dependency parses, and coreference.

Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Supports word inflection (pluralization and singularization) and lemmatization, as well as spelling correction. Add new models or languages through extensions. Also, it comes with a WordNet integration. If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This downloads only those corpora needed for basic functionality. TextBlob is also available as a conda package.

DocWire SDK, a standout C++20AI driven data processing tool, has received award from SourceForge and strong backing from Microsoft. It handles nearly 100 file types, empowering efficient text extraction, web data extraction, and document analysis. For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document format support and the ability to extract valuable insights from email boxes, databases, and websites using cutting-edge AI. DocWire SDK aims to expand its capabilities, focusing on versatile data extraction, platform support, and seamless integration with various systems. DocWire SDK is dedicated to streamlining data processing, reducing development time and costs, and harnessing the potential of AI. Its advancements promise a superior experience compared to its predecessor, DocToText.

dbacl is a general purpose digramic Bayesian text classifier. It can learn text documents you provide, and then compare new input with the learned categories. It can be used for spam filtering, or within your own shell scripts. Sometimes it plays che

TextMarker is now developed and hosted at Apache UIMA (http://uima.apache.org/textmarker.html). TextMarker is a UIMA-based tool for information extraction and more. The full featured editor of the rule language and the build process of UIMA descriptors are complemented with components for visualization, explanation, testing and rule learning.

There are many tools available in market which will provide spell correction or grammer correction while making documents, but very few tools are available which are providing sentence completion according to previously entered text. But this all are providing sentence complition suggestion for sentences which are oftenly or regularly used by all people in same manner. But in reality style of writing changes person to person. While our aim is to provide a sentence suggestion tool which will give suggestion to complete the sentence according previously enterd data by the user. Output or suggestion for same sentence or word will change person to person according to previously entered data by that person. So, it will be very easy to type any document, sms, mail, chatting etc.

WebDjVuTextEd allows to edit the text layer of OCR'ed DjVu documents in a web browser. You can modify the structure (paragraphs, lines, words...) create, delete, edit text nodes, modify their container box by mouse, and run a spellchecker. The program does not directly read the DjVu files, it requires exported XML text data and images. When using without a webserver, you can open and save local files, but cannot take advantages of auto-save and spell checking. Note that current SVN version has much more features than V1.0!

Version 1.04. Applies and builds upon Apache OpenNLP. For English, French and German files. JavaFX Application, runs with Oracle Java Runtime Environment version 8 that is including JavaFX. NLP extensions: - Divide sentences in subclauses: segmentation. - Divide plain text: subject, predicate, object. - Count words: stemming. - Search for similar content: pdf's. Gives out subject, predicate and object of sentences of pdf and plain text files. Provides comfortable GUI. Automatic language detection.

A python module that provides algorithms for advanced search - basically all you need to build a search engine.

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

Auvai is a Java API and Java Swing based application for Text to Speech conversion of Unicode Tamil. Future direction of this API and application is to support Text to Speech conversion for all "Indic" languages.

The book index generator generate the back-of-book index for Thai book automatically.

Concrete Voice is a text to speech program. It can read the time, anounce weather, read text file, save text files to audio files, open any text file (supports all text encoding formats) and many more advance stuff!

A Java application for statistical analysis and systematic manipulation of natural language texts.

A simple intelligent editor.

"Java Artificial Intelligence Markup Language PAD" is a tool that manages ProgramD AI (on local or remote machines) and AIML files with real-time previews and it provides a network support to test AI capabilities over many network protocols.

When translating becomes a game ! Text to translate can be graphically selected. Several dictionnaries can be sorted according to the context. A large choice of matching strategies is available. The OCR engine is tunable.

Leseratte is a Java parser for German written language. Currently, it contains a German lexicon (based on the Wiktionary), inflexion rules, a grammar and a parser. (Semantics component planned.) Usable as a Java library, also provides a graphical UI.

This project is devoted to the development of natural language processing tools and resources for the Lingala language, which is spoken by tens of millions of people in central Africa.

Pylero is an open-source Python-based text generator.

The Information Extraction Plugin allows the use of information extraction techniques within RapidMiner. It can be seen as an interface between natural language and IE- or datamining-methods, by extracting interesting information out of documents.

SemNotes is a semantic note taking tool for KDE4, built on top of Nepomuk-KDE. The tool is still under development, but it is already usable, provided that KDE4 is installed and the Nepomuk running.

TF-IDF.jar is a Java Archive file to measure TF-IDF of each document in a document collection (corpus). The jar can be used to (a) get all the terms in the corpus (b) get the document frequency (DF) and inverse document frequency (IDF) of all the terms in the corpus (c) get the TF-IDF of each document in the corpus (d) get each term with their frequency (no. of presence), term frequency (TF) and TF-IDF in every document