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Stanford NLP Group

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44 reviews
  • 8 profiles
  • 6 categories
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4.1
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ConvNetJS

13 reviews

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser.

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Stanford CoreNLP

10 reviews

Stanford CoreNLP provides a set of natural language analysis tools that can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.

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Stanford Part-Of-Speech Tagger

10 reviews

Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'.

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Stanford SPIED

6 reviews

Stanford Pattern-based Information Extraction and Diagnostics (SPIED) is a pattern-based entity extraction and visualization that provides code for two components, Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion and Visualizing and diagnosing the output from one to two systems.

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Stanford Word Segmenter

2 reviews

Stanford Word Segmenter currently supports Arabic and Chinese that provided segmentation schemes have been found to work well for a variety of applications the system requires Java 1.8+ to be installed, it recommend at least 1G of memory for documents that contain long sentences. For files with shorter sentences (e.g., 20 tokens), decrease the memory requirement by changing the option java -mx1g in the run scripts.

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Stanford University Unstructured

1 review

Stanford University Unstructured is an open-source framework for computational fluid dynamics simulation and optimal shape design.

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Tregex, Tsurgeon and Semgrex

1 review

Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions"). Tregex comes with Tsurgeon, a tree transformation language. Also included from version 2.0 on is a similar package which operates on dependency graphs (class SemanticGraph, called semgrex.

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Stanford Phrasal

1 review

Stanford Phrasal is a statistical phrase-based machine translation system, written in Java that provides much the same functionality as the core of Moses it 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.

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Stanford NLP Group Reviews

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1
Sushil K.
SK
Sushil K.
Data Scientist | KPMG | Ex-Tiger Analytics | Ex-IBM | Machine Learning | Deep Learning | Statistical Modelling
04/18/2025
Validated Reviewer
Review source: G2 invite
Incentivized Review

What Makes Stanford SPIED Stand Out?

it best for Pattern-based Analysis. practical solutions that improve result.
Verified User in Consulting
UC
Verified User in Consulting
04/08/2025
Validated Reviewer
Review source: G2 invite
Incentivized Review

Might be outdated for today's LLMs based world

Good features for traditional NLP tools and easy to use
Verified User in Architecture & Planning
UA
Verified User in Architecture & Planning
04/08/2025
Validated Reviewer
Review source: G2 invite
Incentivized Review

Best for Pattern-based entity learning

Usually, it consists of 2 parts one is pattern-based learning and another one is visualizing the output of one or two entity learning systems in their 2 components i like second one visualizing the output

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What is Stanford NLP Group?

The Stanford NLP Group is a renowned research entity specializing in natural language processing within the Computer Science Department at Stanford University. This group focuses on advancing the field of computational linguistics, developing cutting-edge algorithms and models for understanding, processing, and generating human language. Their work spans various applications, including machine translation, sentiment analysis, information retrieval, and more. The group is also known for creating widely-used NLP tools and resources, such as the Stanford CoreNLP library.

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