stanford-nlp

What is the best tool that can do text simplification using Java? Here is an example of text simplification: John, who was the CEO of a company, played golf. ↓ John played golf. John was the CEO of a company. Khairul I see your problem as a task of converting complex or compound sentence into simple sentences. Based on literature Sentence Types , a simple sentence is built from one independent clause. A compound and complex sentence is built from at least two clauses. Also, clause must have subject and verb. So your task is to split sentence into clauses that form your sentence. Dependency

All I want to do is find the sentiment (positive/negative/neutral) of any given string. On researching I came across Stanford NLP. But sadly its in Java. Any ideas on how can I make it work for python? Use py-corenlp Download Stanford CoreNLP The latest version at this time (2018-10-23) is 3.9.2: wget https://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip https://nlp.stanford.edu/software/stanford-english-corenlp-2018-10-05-models.jar If you do not have wget , you probably have curl : curl https://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip -O https://nlp

Hell everyone! I'm using the Stanford Core NLP package and my goal is to perform sentiment analysis on a live-stream of tweets. Using the sentiment analysis tool as is returns a very poor analysis of text's 'attitude' .. many positives are labeled neutral, many negatives rated positive. I've gone ahead an acquired well over a million tweets in a text file, but I haven't a clue how to actually train the tool and create my own model. Link to Stanford Sentiment Analysis page "Models can be retrained using the following command using the PTB format dataset:" java -mx8g edu.stanford.nlp.sentiment

I am trying to extract list of persons and organizations using Stanford Named Entity Recognizer (NER) in Python NLTK. When I run: from nltk.tag.stanford import NERTagger st = NERTagger('/usr/share/stanford-ner/classifiers/all.3class.distsim.crf.ser.gz', '/usr/share/stanford-ner/stanford-ner.jar') r=st.tag('Rami Eid is studying at Stony Brook University in NY'.split()) print(r) the output is: [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'), ('Brook', 'ORGANIZATION'), ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'LOCATION')]