HomeNamed Entity RecognitionText Analysis Online no longer provides NLTK Stanford NLP API Interface
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Text Analysis Online no longer provides NLTK Stanford NLP API Interface, but keep the related demo just for testing:

NLTK Stanford POS Tagger: http://textanalysisonline.com/nltk-stanford-postagger

NLTK Stanford Named Entity Recognizer: http://textanalysisonline.com/nltk-stanford-ner

NLTK Stanford Named Entity Recognizer for 7Class: http://textanalysisonline.com/nltk-stanford-ner-7class

NLTK Stanford Named Entity Recognizer for Caseless: http://textanalysisonline.com/nltk-stanford-ner-caseless

NLTK Stanford Parser: http://textanalysisonline.com/nltk-stanford-parser

We recommend you use the original Stanford NLP Tools by Java: http://nlp.stanford.edu/software/

Which include:

Stanford CoreNLP
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. See also: Stanford Deterministic Coreference Resolution, the online CoreNLP demo, and the CoreNLP FAQ.
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. See also:Online parser demo, the Stanford Dependencies page, neural-network dependency parser documentation, and Parser FAQ.
Stanford POS Tagger
A maximum-entropy (CMM) part-of-speech (POS) tagger for English, Arabic, Chinese, French, German, and Spanish, in Java.
Stanford Named Entity Recognizer
A Conditional Random Field sequence model, together with well-engineered features for Named Entity Recognition in English, Chinese, German, and Spanish. Online NER demo.
Stanford Word Segmenter
A CRF-based word segmenter in Java. Supports Arabic and Chinese.
Stanford Classifier
A machine learning classifier, with good feature templates for text categorization. Provides a softmax (a.k.a., maximum entropy or multiclass logistic regression) classifier, Naive Bayes, and other options.
Tregex, Tsurgeon, and Semgrex
Tools for matching patterns in linguistic trees (following the tgrep/tgrep2 tradition), a GUI for this, and a tree-transformation utility built on top of this matching language. Also, a similar utility for matching patterns in dependency graphs.
A state-of-the-art phrase-based machine translation system.
Stanford EnglishTokenizer
A fast tokenizer for English text (producing Penn Treebank tokenization, roughly)
Stanford TokensRegex
A tool for matching regular expressions over tokens.
Stanford Temporal Tagger (SUTime)
A rule-based temporal tagger for English text. Online SUTime demo.
Stanford Pattern-based Information Extraction and Diagnostics (SPIED)
A boostrapped pattern-based entity extraction system.
Stanford Relation Extractor
A tool for extracting relations between entities.
GloVe: Global Vectors for Word Representations
Software in C for learning state-of-the-art distributed word representations, and a number of sets of pre-trained word vectors.
Topic Modeling Toolbox (TMT)
A suite of topic modeling tools for social scientists and others who wish to perform analysis on datasets that have a substantial textual component. Unfortunately, this software is no longer developed or supported.
Stanford Biomedical Event Parser (SBEP)
Biomedical Event Extraction for the BioNLP 2009/2011 shared task.

Posted by TextMiner

Deep Learning Specialization on Coursera


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