Core Projects Archive Stanford CoreNLP [ backup download page ] An integrated suite of natural language processing tools for English, Spanish, and mainland Chinese in Java, including tokenization, part-of-speech tagging, named entity recognition, parsing, and coreference.
Stanza A Python natural language analysis package that provides implementations of fast neural network models for tokenization, multi-word token expansion, part-of-speech and morphological features tagging, lemmatization and dependency parsing using the Universal Dependencies formalism. Pretrained models are provided for more than 70 human languages. In addition, it is able to call the CoreNLP Java package and inherits additonal functionality from there, such as constituency parsing, coreference resolution, and linguistic pattern matching.
Stanford Parser Implementations of probabilistic natural language parsers in Java: PCFG and dependency parsers, a lexicalized PCFG parser, a super-fast neural-network dependency parser, and a deep learning reranker. Online NER demo. Stanford Coreference Resolution Rule-based, statistical, and neural models for nominal coreference resolution in Java. Supports Arabic and Chinese. Stanford Classifier A machine learning classifier, with good feature templates for text categorization.
Provides a softmax a. Also, a similar utility for matching patterns in dependency graphs. Stanford TokensRegex A tool for matching regular expressions over tokens. Online SUTime demo. Stanford Relation Extractor A tool for extracting relations between entities. We release our codebase which produces state-of-the-art results in various translation tasks such as English-German and English-Czech. In addtion, to encourage reproducibility and increase transparency, we release our preprocessed data and pretrained models as well.
Stanford Open Information Extraction A tool for extracting open domain relation triples; e. We also distribute a number of sets of pre-trained word vectors. Also, one of the best Machine Learning courses is taught by a Stanford professor on Coursera.
Check it out along with other great resources. It can be used to process text, either locally or on remote systems, which can remove a tremendous burden from your local device. It provides processing functions such as tokenization, part-of-speech tagging, chunking, named-entity tagging, lemmatization, dependency and constituency parsing, and semantic role labeling.
Overall, this is a great tool for research, and it has a lot of components that you can explore. I'm not sure it's great for production workloads, but it's worth trying if you plan to use Java. What are your favorite open source tools and libraries for NLP?
Please share in the comments—especially if there's one I didn't include. It will be interesting to see if any of your Java NLP solutions are struggling similarly. Thank you so much! The Java space in this area is really interesting. It was so active a few years ago, and now a lot of the work seems to be moving into companies rather than purely open source. I think this has slowed progress there while at the same time Python has become THE language for data science.
I wonder if a few more stumbling blocks with lead to even less Java for data science. I know almost all my work in that area is now done in Python or Node for lighter stuff. There's obviously still some C and Java and other languages working on the backend and with really large datasets.
Because I'm deeply invested in the whole JVM end of things, when I need to do something quick and dirty that can rely on Java-based tools, I use Groovy.
But for whatever reason, this approach hasn't really caught on in the same way that Python has as a convenient framework for accessing powerful toolchains. Evidence of this is really clear, I think - compare the number of projects willing to write some high-performance toolchain in C so that it can be called from Python, vs the number projects willing to write some high-performance toolchain in Java so that it can be called from Groovy Kotlin could help as an alternative way of leveraging Java code base.
I guess Scala already has? Take a look at a dozen options for your next NLP application. Get the highlights in your inbox every week. Textacy This tool may have the best name of any library I've ever used. Node tools Retext Retext is part of the unified collective. Compromise Compromise certainly isn't the most sophisticated tool. Natural Natural includes most functions you might expect in a general NLP library.
Topics Tools. About the author. He was the Chief Architect at the National Association of Insurance Commissioners leading their technical and cultural transformation. More about me. Recommended reading 9 ways to use open source every day.
Essential open source tools for an academic organization. Explore waterways with this open source nautical navigation tool. SaaS tools are ready-to-use and powerful cloud-based solutions that can be implemented with low or no code. SaaS platforms often offer pre-trained NLP models that can be used code-free, and APIs that are geared more towards those who want a more flexible, low-code, option, e.
Open-source libraries, on the other hand, are free, flexible, and allow you to fully customize your NLP tools. Luckily, though, most of them are community-driven frameworks, so you can count on plenty of support. MonkeyLearn is a user-friendly, NLP-powered platform that helps you gain valuable insights from your text data. To get started, you can try one of the pre-trained models , to perform text analysis tasks such as sentiment analysis, topic classification, or keyword extraction.
For more accurate insights, you can build a customized machine learning model tailored to your business. Aylien is a SaaS API that uses deep learning and NLP to analyze large volumes of text-based data, such as academic publications, real-time content from news outlets and social media data. You can use it for NLP tasks like text summarization, article extraction, entity extraction, and sentiment analysis, among others.
One of its key features is Natural Language Understanding, which allows you to identify and extract keywords, categories, emotions, entities, and more. The Google Cloud Natural Language API provides several pre-trained models for sentiment analysis, content classification, and entity extraction, among others.
Also, it offers AutoML Natural Language, which allows you to build customized machine learning models. As part of the Google Cloud infrastructure, it uses Google question-answering and language understanding technology.
Focused on research and education in the NLP field, NLTK is bolstered by an active community, as well as a range of tutorials for language processing , sample datasets, and resources that include a comprehensive Language Processing and Python handbook. With a modular structure, NLTK provides plenty of components for NLP tasks, like tokenization, tagging, stemming, parsing, and classification, among others.
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