stanford-nlp

I am trying to parse book-length blocks of text with StanfordNLP. The http requests work great, but there is a non-configurable 100KB limit to the text length, MAX_CHAR_LENGTH in StanfordCoreNLPServer.java. For now, I am chopping up the text before I send it to the server, but even if I try to split between sentences and paragraphs, there is some useful coreference information that gets lost between these chunks. Presumably, I could parse chunks with large overlap and link them together, but that seems (1) inelegant and (2) like quite a bit of maintenance. Is there a better way to configure

I'm experimenting with Stanford Core NLP for named entity recognition. Some named entities consist of more than one token, for example, Person: "Bill Smith". I can't figure out what API calls to use to determine when "Bill" and "Smith" should be considered a single entity, and when they should be two different entities. Is there some decent documentation somewhere which explains this? Here's my current code: InputStream is = getClass().getResourceAsStream(MODEL_NAME); if (MODEL_NAME.endsWith(".gz")) { is = new GZIPInputStream(is); } is = new BufferedInputStream(is); Properties props = new

Is it possible to hava a Java alternative to NLTK in order to 'verbify' words as can be seen in this question? Convert words between verb/noun/adjective forms For example I would like to convert born to birth, since when using Wordnet Similarity, the algorithm does not show that born and birth are very similar. I would like to therefore convert either born to birth or vice versa. In order to have much more similar words. What do you suggest? I found some tools but I'm not sure if they can do this: - NTLK (only python I guess) - OpenNlp - Stanford-Nlp - Simple NLG Thank you A quick and dirty

I'm a newbie in NLP and Python. I'm trying to extract a subset of noun phrases from parsed trees from StanfordCoreNLP by using the Tregex tool and the Python subprocess library. In particular, I'm trying to find and extract noun phrases that match the following pattern: '(NP[$VP]>S)|(NP[$VP]>S\n)|(NP\n[$VP]>S)|(NP\n[$VP]>S\n)' in the Tregex grammar. For example, below is the original text, saved in a string named "text": text = ('Pusheen and Smitha walked along the beach. "I want to surf", said Smitha, the CEO of Tesla. However, she fell off the surfboard') After running the StanfordCoreNLP

I am planning to use Named Entity Recognition (NER) technique to identify person names (most of which are Indian names) from a given text. I have already explored the CRF-based NER model from Stanford NLP, however it is not quite accurate in recognizing Indian names. Hence I decided to create my own custom NER model via supervised training. I have a fair idea of how to create own NER model using the Stanford NER CRF, but creating a large training corpus with manual annotation is something I would like to avoid, as it is a humongous effort for an individual and secondly obtaining diverse people