Search Results for "algorithms"

...Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported. It’s still in the early stages, so we’re very interested in bug reports, contributions and the like. Note that many algorithms from Rob Ellis’s node-nltools are being merged into this project and will be maintained from here onward. While most of the algorithms are English-specific, contributors have implemented support for other languages. Russian stemming has been added and Spanish stemming has been added, as well. Stemming and tokenizing in more languages have been added. ...

Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster?

HanLP is a multilingual Natural Language Processing (NLP) library composed of a series of models and algorithms. Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis.

neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu...

What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. This is not...

...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.

...Unlike database fields, which are easily queried, text contains unstructured data that must be parsed for key objects that can be transformed in to powerful metrics. Litersta - textual analytics - software leverages statistical algorithms to programmatically locate, and extract, overall document sentiment, word frequencies, and document similarities. The Litersta web application runs locally on your server - behind your firewall. This strategy keeps your data confidential and secure.