stanford-nlp

I downloaded stanford NER 3.4.1 , unpacked it, and tried to run named entity recognition on a local file using the default (provided) trained model. I got this: `java.io.FileNotFoundException: /u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters (No such file or directory) at edu.stanford.nlp.io.IOUtils.inputStreamFromFile(IOUtils.java:481)` What's wrong and how can I fix it? It turns out that the provided models use "distributional similarity features" that require a .clusters file at a location specified in the compressed model file (tricky to change). If you're on the stanford network,

I have a CoNLL-X format treebank and the corresponding binary parse tree for each sentence and I want to convert it into a PTB format. Is there any converters or can anyone shed light on the PTB format? dmcc There's been a number of efforts to convert from dependencies (representable in CoNLL-X format) to constituents (representable in Penn Treebank , or PTB, format). Two recent papers and their code: Transforming Dependencies into Phrase Structures (Kong, Rush, and Smith, NAACL 2015). Code . Parsing as Reduction (Fernandez-Gonzalez and Martins, ACL 2015). Code . 来源： https://stackoverflow.com

I am trying to use Stanford POS Tagger in NLTK but I am not able to run the example code given here http://www.nltk.org/api/nltk.tag.html#module-nltk.tag.stanford import nltk from nltk.tag.stanford import POSTagger st = POSTagger(r'english-bidirectional-distim.tagger',r'D:/stanford-postagger/stanford-postagger.jar') st.tag('What is the airspeed of an unladen swallow?'.split()) I have already added environment variables as CLASSPATH = D:/stanford-postagger/stanford-postagger.jar STANFORD_MODELS = D:/stanford-postagger/models/ Here is the error I keep getting Traceback (most recent call last):

Stanford CoreNLP provides coreference resolution as mentioned here , also this thread , this , provides some insights about its implementation in Java. However, I am using python and NLTK and I am not sure how can I use Coreference resolution functionality of CoreNLP in my python code. I have been able to set up StanfordParser in NLTK, this is my code so far. from nltk.parse.stanford import StanfordDependencyParser stanford_parser_dir = 'stanford-parser/' eng_model_path = stanford_parser_dir + "stanford-parser-models/edu/stanford/nlp/models/lexparser/englishRNN.ser.gz" my_path_to_models_jar =

This may be a silly question, but how does one iterate through a parse tree as an output of an NLP parser (like Stanford NLP)? It's all nested brackets, which is neither an array nor a dictionary or any other collection type I've used. (ROOT\n (S\n (PP (IN As)\n (NP (DT an) (NN accountant)))\n (NP (PRP I))\n (VP (VBP want)\n (S\n (VP (TO to)\n (VP (VB make)\n (NP (DT a) (NN payment)))))))) This particular output format of the Stanford Parser is call the "bracketed parse (tree)". It is supposed to be read as a graph with words as nodes (e.g. As, an, accountant) phrase/clause as labels (e.g. S,

So I got the "standard" Stanford Parser to work thanks to danger89's answers to this previous post, Stanford Parser and NLTK . However, I am now trying to get the dependency parser to work and it seems the method highlighted in the previous link no longer works. Here is my code: import nltk import os java_path = "C:\\Program Files\\Java\\jre1.8.0_51\\bin\\java.exe" os.environ['JAVAHOME'] = java_path from nltk.parse import stanford os.environ['STANFORD_PARSER'] = 'path/jar' os.environ['STANFORD_MODELS'] = 'path/jar' parser = stanford.StanfordDependencyParser(model_path="path/jar/englishPCFG.ser

NOTE: I am using Python 2.7 as part of Anaconda distribution. I hope this is not a problem for nltk 3.1. I am trying to use nltk for NER as import nltk from nltk.tag.stanford import StanfordNERTagger #st = StanfordNERTagger('stanford-ner/all.3class.distsim.crf.ser.gz', 'stanford-ner/stanford-ner.jar') st = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz') print st.tag(str) but i get Exception in thread "main" java.lang.NoClassDefFoundError: org/slf4j/LoggerFactory at edu.stanford.nlp.io.IOUtils.<clinit>(IOUtils.java:41) at edu.stanford.nlp.ie.AbstractSequenceClassifier