Open Source Linux Natural Language Processing (NLP) Tools - Page 6
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Natural Language Processing (NLP) Tools for Linux
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DeepSparse
Sparsity-aware deep learning inference runtime for CPUs
A sparsity-aware enterprise inferencing system for AI models on CPUs. Maximize your CPU infrastructure with DeepSparse to run performant computer vision (CV), natural language processing (NLP), and large language models (LLMs). -
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Delta ML
Deep learning based natural language and speech processing platform
DELTA is a deep learning-based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. Uniform I/O interfaces and no changes for new models. -
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Duckling
Language, engine, and tooling for testing composable language rules
Duckling is a Haskell library developed by Facebook for parsing and normalizing natural language expressions into structured data. It supports a wide range of entities such as dates, times, durations, distances, temperatures, numbers, and currencies. Designed for use in conversational agents, chatbots, and natural language processing applications, Duckling converts fuzzy user input into a consistent and machine-readable format. It features multi-language support and is widely used in production environments requiring robust entity extraction. -
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EORS
rational agent
This project aims at creating rationally thinking agents. The agent gather information through command line or network and stores it in its memory. It uses Stanford's NLP library to understand the language statements. -
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Ecco
Explain, analyze, and visualize NLP language models
Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent. -
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Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs. Across a variety of natural-language-processing datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability. -
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English-Khmer S. Machine Translation
English-Khmer Automatic Statistic Machine Translation (SMT)
Automatic Machine Translation from English to Khmer project is the first effort in Natural Language Processing field for translating English to Khmer (Cambodian) language. This project uses Domy CE, an open source SMT toolkit, for training parallel corpus and web technologies such as Python, Apache2, HTML, XML, and XSLT for developing web-based application. This project is developed by Ms. Kim Sokphyrum (DU) and Ms. Suos Samak (Jamia), under Supervision of Mr. Javier Sola, a Program Manager at Open Institute (OI), Cambodia, Dr. Vasudha Bhatnagar, an Assistant professor and a Head of Computer Science at University of Delhi (DU), New Delhi, India. and Dr. Suraiya Jabin, an Assistant professor at Jamia Millia Islamia University, New Delhi, India. -
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FALCON - Text Search Java Project
JSON based text search Java Project
----------------- - What is it? - ----------------- The "Falcon Search" is a JAVA API and tool to search inside the documents. It was originally started to search the content in pdf files under the project "HAWK Search". Searching with this tool is query-based not word-based as in most of the document search tools OR document readers. It also takes care of jumbling of words within query and spelling mistakes. Commonly used techniques in this project are Natural Language Processing, Information Extraction and Question-Answering Architecture. ---------------------- - Latest Version - ---------------------- Details of latest version can be found on project website - http://geekdadaji.com --------------------------- - CONTACT DETAILS - --------------------------- CREATOR : SWAPNIL A JADHAV (saj1919) EMAIL ID : dadajibudhau@gmail.com WEBSITE : http://geekdadaji.com LICENSE : CC BY-NC 4.0 -
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FARM
Fast & easy transfer learning for NLP
FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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FalaBrasil
Resources for speech processing in Brazilian Portuguese
The FalaBrasil Group provides free tools and resources for speech and natural language processing in Brazilian Portuguese, most of them under the BSD license. Tools include mainly scripts to do all sort of things with audio and text, whereas resources include ready-to-used acoustic and languages models, phonetic dictionaries, etc. -
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FastRAG
Efficient Retrieval Augmentation and Generation Framework
fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval. fastRAG is designed to empower researchers and developers with a comprehensive tool set for advancing retrieval augmented generation. -
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FemtoThnk models higher-level brain functions. It is an artificial intelligence model that includes features such as natural language processing (any ISO Latin alphabet), problem solving, dreaming, pattern sensing and hierarchical organization.
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G-Asks is a question generation system, developed by LATTE(Learning and Affect Technologies Engineering) research group at The University of Sydney. It uses Natural Language Processing techniques and Machine learning algorithms to generate specific trigger questions. If you use this software in a publication, please cite the paper 2. 1.Ming Liu and Rafael A. Calvo (2012) “Using Information Extraction to Generate Trigger Question for Academic Writing Support”, 11th International Conference on Intelligent Tutoring Systems, Crete, Springer LNCS 7315, p.360-369. 2.Ming Liu, Rafael A. Calvo, Anindito Aditomo and Luiz Augusto Pizzato (2012), “Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support”, IEEE Transactions on Learning Technologies, vol. 5, no. 3, pp. 251-263.
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GT NLP Class
Course materials for Georgia Tech CS 4650 and 7650
This repository contains lecture notes, slides, assignments, and code for a university-level Natural Language Processing course. It spans core NLP topics such as language modeling, sequence tagging, parsing, semantics, and discourse, alongside modern machine learning methods used to solve them. Students work through programming exercises and problem sets that build intuition for both classical algorithms (like HMMs and CRFs) and neural approaches (like word embeddings and sequence models). The materials emphasize theory grounded in practical experimentation, often via Python notebooks or scripts that visualize results and encourage ablation studies. Clear organization and self-contained examples make it possible to follow along outside the classroom, using the repo as a self-study resource. For learners and instructors alike, the course provides a coherent path from foundational linguistics to current techniques, with reproducible code that makes concepts concrete. -
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This package contains different tools to add NLP capabilities for Lucene 4.x (it has been tested using Lucene version from 4.6.x to 4.8.1). Although it was originally developed for German, it is, mostly, language independent. It allows the user to lemmatize words to be indexed, to weight termy ba their parts of speech (e.g. weighting nouns mor hevaily than pronouns), and to add synonyms taken from GermaNet or a list you provide to the search index and thereby increase recall of lucene.
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GluonNLP
NLP made easy
GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training embeddings on custom datasets. Fasttext models trained with the library of Facebook research are exported both in text and a binary format. Unlike the text format, the binary format preserves information about subword units and consequently supports the computation of word vectors for words unknown during training (and not included in the text format). Besides training new fastText embeddings with Gluon NLP it is also possible to load the binary format into a Block provided by the Gluon NLP toolkit. -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of Graph4NLP is shown in the following figure, where boxes with dashed lines represent the features under development. Graph4NLP consists of four different layers: 1) Data Layer, 2) Module Layer, 3) Model Layer, and 4) Application Layer. Graph4nlp aims to make it incredibly easy to use GNNs in NLP tasks (check out Graph4NLP Documentation). -
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Graphical Grammar Studio
An user friendly grammar tool for natural language processing tasks
Full documentation with tutorials is included in the download package. Graphical Grammar Studio is a tool for applying grammars which behave as words acceptors/consumers and annotators. GGS grammars can be used to find and annotate sequences of words which respect certain conditions, in a given input. Its purpose is for creating NLP tools like phrase chunkers, named entity finders, pronoun co-reference solvers etc. A grammar is represented by a state machine which can be visualized, edited and applied. A grammar is organized in graphs of nodes. Nodes are used for consuming words from the input, for executing jumps to other graphs in the grammar or for creating annotations etc. GGS has a unique feature: It allows the user to write JavaScript code to be executed for nodes of the grammar. This is useful for checking grammatical agreements but not only. The user can: declare variables (including complex js structures), check for boolean conditions, use variables in annotations etc. -
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HAWK - PDF Text Search Java Project
No more support for this project - TAKE A LOOK AT FALCONSEARCH
No more support for this project - TAKE A LOOK AT FALCONSEARCH "https://sourceforge.net/projects/falcontextsearch/" -
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Hazm
Persian NLP Toolkit
Hazm is a natural language processing (NLP) library for Persian text, offering various tools for text preprocessing, tokenization, part-of-speech tagging, and more. -
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IceNLP is an open source Natural Language Processing (NLP) toolkit for analyzing and processing Icelandic text. The toolkit is implemented in Java.
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The Infomap NLP software performs automatic indexing of words and documents from free-text corpora, using a variant of LSA to enable information retrieval and other applications. It was developed by the Infomap Project at Stanford University's CSLI.
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Infouma OS
Linux-Mint based Distro made for who works in the Digital Humanities.
Infouma OS is a Linux-Mint based Distro made for who works in the Digital Humanities. Web development, natural language processing, digital publishing and multimedial productivity. All included in this great distro. Infouma OS has a Beautiful design, and it is incredibly complete. Made by (info)humans to info(humans). -
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JFex is a feature extraction application for Natural Language Processing systems based on machine learning techniques.
