stanford-nlp

When using the parser or for the matter any of the Annotation in Core NLP, is there a way to access the probability or the margin of error? To put my question into context, I am trying to understand if there is a way programmatically to detect a case of ambiguity. For instance in the sentence below the verb desire is detected as a noun. I would like to be able to know so kind of measure I can access or calculate from the Core NLP APi to tell me there could be an ambiguity. (NP (NP (NNP Whereas)) (, ,) (NP (NNP users) (NN desire) (S (VP (TO to) (VP (VB sell)))))) 来源： https://stackoverflow.com

I am working on a project that requires me to tag tokens using nltk and python. So I wanted to use this. But came up with a few problems. I went through a lot of other already asked questions and other forums but I was still unable to get a soultion to this problem. The problem is when I try to execute the following: from nltk.tag import StanfordPOSTagger st = StanfordPOSTagger('english-bidirectional-distsim.tagger') I get the following: Traceback (most recent call last): `File "<pyshell#13>", line 1, in <module> st = StanfordPOSTagger('english-bidirectional-distsim.tagger')` `File "C:\Users

If I want to train the Stanford Neural Network Dependency Parser for another language, there is a need for a "treebankLanguagePack"(TLP) but the information about this TLP is very limited: particularities of your treebank and the language it contains If I have my "treebank" in another language that follows the same format as PTB, and my data is using CONLL format. The dependency format follows the "Universal Dependency" UD. Do I need this TLP? As of the current CoreNLP release, the TreebankLanguagePack is used within the dependency parser only to 1) determine the input text encoding and 2)

I am currently trying to use the Stanford NER system and I am trying to see what features can be extracted through setting of the flags in a properties file. It seems that the features documented at http://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/ie/NERFeatureFactory.html are not comprehensive. For example, all the feature flags related to dist similarity and clustering are not included (e.g. useDistSim, etc.). Is there a more complete list of all the features and corresponding flags that is available somewhere? Thanks for the help! At present, no. You need to look through the

I am working on a project involving anaphora resolution via Hobbs algorithm. I have parsed my text using the Stanford parser, and now I would like to manipulate the nodes in order to implement my algorithm. At the moment, I don't understand how to: Access a node based on its POS tag (e.g. I need to start with a pronoun - how do I get all pronouns?). Use visitors. I'm a bit of a noob of Java, but in C++ I needed to implement a Visitor functor and then work on its hooks. I could not find much for the Stanford Parser's Tree structure though. Is that jgrapht? If it is, could you provide me with

I have written below code using stanford nlp packages. GenderAnnotator myGenderAnnotation = new GenderAnnotator(); myGenderAnnotation.annotate(annotation); But for the sentence "Annie goes to school", it is not able to identify the gender of Annie. The output of application is: [Text=Annie CharacterOffsetBegin=0 CharacterOffsetEnd=5 PartOfSpeech=NNP Lemma=Annie NamedEntityTag=PERSON] [Text=goes CharacterOffsetBegin=6 CharacterOffsetEnd=10 PartOfSpeech=VBZ Lemma=go NamedEntityTag=O] [Text=to CharacterOffsetBegin=11 CharacterOffsetEnd=13 PartOfSpeech=TO Lemma=to NamedEntityTag=O] [Text=school

I've trained a POS tagger and neural dependency parser with Stanford corenlp. I can get them to work via command line, and now would like to access them via a server. However, the documentation for the server doesn't say anything about using custom models. I checked the code and didn't find any obvious way of supplying a configuration file. Any idea how to do this? I don't need all annotators, just the ones I trained. Yes, the server should (in theory) support all the functionality of the regular pipeline. The properties GET parameter is translated into the Properties object you would normally

I am analysing a few million emails. My aim is to be able to classify then into groups. Groups could be e.g.: Delivery problems (slow delivery, slow handling before dispatch, incorrect availability information, etc.) Customer service problems (slow email response time, impolite response, etc.) Return issues (slow handling of return request, lack of helpfulness from the customer service, etc.) Pricing complaint (hidden fee's discovered, etc.) In order to perform this classification, I need a NLP that can recognize the combination of word groups like: "[they|the company|the firm|the website|the