Open Source Linux Natural Language Processing (NLP) Tools - Page 8
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Natural Language Processing (NLP) Tools for Linux
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU-related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-processing, build model, train, validate, infer, save or load a model. -
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NLP Best Practices
Natural Language Processing Best Practices & Examples
In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems. -
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NLP.js
An NLP library for building bots
NLP.js is an NLP library for building bots, with entity extraction, sentiment analysis, automatic language identifier, and much more. "NLP.js" is a general natural language utility for nodejs. Search the best substring of a string with less Levenshtein distance to a given pattern. Get stemmers and tokenizers for several languages. Sentiment Analysis for phrases (with negation support). Named Entity Recognition and management, multi-language support, and acceptance of similar strings, so the introduced text does not need to be exact. Natural Language Processing Classifier, to classify an utterance into intents. NLP Manager, a tool able to manage several languages, the Named Entities for each language, the utterances, and intents for the training of the classifier, and for a given utterance return the entity extraction, the intent classification and the sentiment analysis. -
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NLP4J library is a toolset written in Java for Natural Language Processing. This version is oriented to Document Classification and uses Naive Bayes, TF-IDF, etc. There are also pre-processing tools.
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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NXTAI's main objective is to create a new generation of natural language processing program by using a unique neural networking model. To demonstrate the algorithm works, A chatbot will be developed as a classical example of how it can be used.
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Natural Language Analysis with Ngrams
NLP tool for statistical analysis of words, sentences, documents
Goal of this project is to have a NLP tool that would give statistical analysis results based on Google Ngram data. Furthermore, it is now just a NetBeans project without a final JAR. Furthermore, there will be a github version for anyone who wishes to contribute. In the future versions, user will be able to convert a single word to numerical data, to be able to compare two words and get the comparison data, and to be able to do the same for the sentences, paragraphs and documents. I will JAR-it once I decide that it can be called a final release. This project was made by creating a corpus from the Google Ngrams data for English Language, version 20120701. EOWL list of English words was used to filter-out the words from Ngrams data. For each year, per word, the data was added and calculated to describe the average appearance of a word per document for a given year. Before using this program, you MUST download the corpus. -
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Collection of Python scripts providing interactions between the sociological investigation platform and webservices (such as NLP, search engine, web database).
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NeuroNER
Named-entity recognition using neural networks
Named-entity recognition (NER) aims at identifying entities of interest in the text, such as location, organization and temporal expression. Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. During the training, NeuroNER allows monitoring of the network. Evaluate the quality of the predictions made by NeuroNER. The performance metrics can be calculated and plotted by comparing the predicted labels with the gold labels. -
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NobleTools
Collection of NLP Tools developed at DBMI at University of Pittsburgh
Collection of NLP Tools developed at Department of BioMedical Informatics at University of Pittsburgh. The set of tools include a generic Terminology and Ontology API, Named Entity Recognition (NER) tool called NobleCoder, Information Extraction (IE) framework, Negation detection and more. -
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The OBO-Annotator is a semantic NLP tool that is designed to give its end-users a great deal of flexibility to combine any number of OBO ontologies from the OBO foundry regardless of their format and use them to annotate text-bases.
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OPTIMA cidoc-crm Semantic Annotation
Semantic annotation of archaeology reports with respect to CIDOC-CRM
The semantic annotation system OPTIMA is the result of Andreas Vlachidis PhD work, (supervised by Prof. Douglas Tudhope, University of Glamorgan, UK). OPTIMA performs the NLP tasks of Named Entity Recognition, Relation Extraction, Negation Detection and Word Sense Disambiguation using hand-crafted rules and SKOS terminological resources (English Heritage Thesauri and Glossaries). The resulted semantic annotations are associated with classes of the (ISO 21127:2006) CIDOC Conceptual Reference Model (CRM) and its archaeological extension, CRM-EH. OPTIMA is also targeted at the detection and recognition of contextual relations between CRM entities. Such relations are modeled with respect to the CRM-EH archaeology extension. The pipeline targets the CIDOC-CRM entities; E19.Physical_Object, E53.Place, E49.Time_Appellation and E57.Material and the CRM-EH entities; EHE1001.Context_Event, EHE1002.Production_Event, EHE1004.Deposition_Event and P45.consists_of material property -
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Open Pandora's Box
Pandora is an artificial intelligent web based bot
Pandora is an artificial intelligent web based bot written in Java. Pandora is a component based AI architecture including, database memory, XML, voice, voice rec, chat, IRC, HTTP, Wiktionary, Freebase, consciousness, language, GUI, applet, web, jsp, Android -
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OpenCCG, the OpenNLP CCG Library, is a collection of natural language processing components and tools which provide support for parsing and realization with Combinatory Categorial Grammar (CCG).
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OpenDMAP (Open Source Direct Memory Access Parser) is a natural language processing (text mining) application: a semantic parser for information extraction.
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OpenDelta
A plug-and-play library for parameter-efficient-tuning
OpenDelta is an open-source parameter-efficient fine-tuning library that enables efficient adaptation of large-scale pre-trained models using delta tuning techniques. OpenDelta is a toolkit for parameter-efficient tuning methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most parameters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora, or any other types of delta tuning with preferred PTMs. -
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OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. OpenPrompt supports loading PLMs directly from huggingface transformers. In the future, we will also support PLMs implemented by other libraries. The template is one of the most important modules in prompt learning, which wraps the original input with textual or soft-encoding sequence. Use the implementations of current prompt-learning approaches.* We have implemented various of prompting methods, including templating, verbalizing and optimization strategies under a unified standard. You can easily call and understand these methods. -
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Osman Arabic Text Readability
Open Source tool for Arabic text readability
We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. The open source Java tool allows users to calculate readability for Arabic text (with and without diacritics). The tool provides methods to split the text into words and sentence, count syllables, Faseeh letters, hard and complex words in addition to adding diacritics (vocalise text). This makes the tool useful for researchers and educators working with Arabic text. All the readability metrics mentioned in Section \ref{calcRead} are included within the open source code, they all work with vocalised and non-vocalised text but based our results presented here we recommend adding the diacritics in by using the addTashkeel() method. See the files sections for the vocalised version of UN Arabic English parallel paragraphs. -
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PORORO
Platform of neural models for natural language processing
pororo performs Natural Language Processing and Speech-related tasks. It is easy to solve various subtasks in the natural language and speech processing field by simply passing the task name. Recognized speech sentences using the trained model. Currently English, Korean and Chinese support. Get vector or find similar words and entities from pretrained model using Wikipedia. -
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ParseManager is a tool which manages parses for natural language processing. It can create, modify and view parses.
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Persica-A new Persian corpus for NLP
This project presents a new corpus for NEWS text analysis in Persian
Lack of multi-application text corpus despite of the surging text data is a serious bottleneck in the text mining and natural language processing especially in Persian language. This project presents a new corpus for NEWS articles analysis in Persian called Persica. NEWS analysis includes NEWS classification, topic discovery and classification, category classification and many more procedures. Dealing with NEWS has special requirements and first of all a valid and reliable corpus to perform the experiments on them. Please use this reference to cite us: @inproceedings{eghbalzadeh2012persica, title={Persica: A Persian corpus for multi-purpose text mining and Natural language processing}, author={Eghbalzadeh, Hamid and Hosseini, Behrooz and Khadivi, Shahram and Khodabakhsh, Ali}, booktitle={Telecommunications (IST), 2012 Sixth International Symposium on}, pages={1207--1214}, year={2012}, organization={IEEE} } -
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Phrasal
Statistical phrase-based machine translation system
Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java. At its core, it provides much the same functionality as the core of Moses. Distinctive features include: providing an easy to use API for implementing new decoding model features, the ability to translating using phrases that include gaps (Galley et al. 2010), and conditional extraction of phrase-tables and lexical reordering models. 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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Prime QA
State-of-the-art Multilingual Question Answering research
PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly downloadable.
