stanford-nlp

I found German parse and pos-tag models which are compatible with Stanford Core NLP. However I was not able to get German lemmatization working. Is there a way to do so? Since the version 3.6 is also German supported. Check it under http://stanfordnlp.github.io/CoreNLP/history.html Sorry, as far as I know no implementation of German lemmatization exists for Stanford CoreNLP. 来源： https://stackoverflow.com/questions/29861925/does-stanford-core-nlp-support-lemmatization-for-german

I am unable to access coreNLP in R on a Mac running High Sierra. I am uncertain what the problem is, but it seems that every time I try again to get coreNLP to work, I am faced with a different error. I have JDK 9.0.4. Please see my code below for what I am attempting to do, and the error that stops me. My previous attempt I was able to get initCoreNLP() to run and load some elements of the packages, but it would fail on others. When I then attempted to run annotateString() , it would throw the error Error Must initialize with 'int CoreNLP'! . I have downloaded and re-downloaded the coreNLP

We are trying to use existing tokenzation sentence splitting and named entity tagging while we would like to use Stanford CoreNlp to additionally provide us with part-of-speech tagging lemmatization and parsing Currently, we are trying it the following way: 1) make an annotator for "pos, lemma, parse" Properties pipelineProps = new Properties(); pipelineProps.put("annotators", "pos, lemma, parse"); pipelineProps.setProperty("parse.maxlen", "80"); pipelineProps.setProperty("pos.maxlen", "80"); StanfordCoreNLP pipeline = new StanfordCoreNLP(pipelineProps); 2) read in the sentences, with a custom

I'm trying to find the semantic labels of english sentences. I am using the Stanford NLP parser. Does it have methods for this? I was going through the documentation but the closest thing I could find was: CoreAnnotations.SemanticWordAnnotation CoreAnnotations.SemanticTagAnnotation No, we currently don't have a semantic role labeling (SRL) system in CoreNLP. Unless you already have a system that explicitly requires semantic role labels, I would recommend taking a look at the Universal Dependencies representation. Despite the fact that this representation is primarily a syntactic representation

I'm trying to extract information from several files using the OpenIE tool from Stanford CoreNLP, it gives an out of memory error when several files are passed to the input, instead of just one. All files have been queued; awaiting termination... java.lang.OutOfMemoryError: GC overhead limit exceeded at edu.stanford.nlp.graph.DirectedMultiGraph.outgoingEdgeIterator(DirectedMultiGraph.java:508) at edu.stanford.nlp.semgraph.SemanticGraph.outgoingEdgeIterator(SemanticGraph.java:165) at edu.stanford.nlp.semgraph.semgrex.GraphRelation$GOVERNER$1.advance(GraphRelation.java:267) at edu.stanford.nlp

I am trying training a custom NER model for multiple entities. Here is the sample training data: count all <START:item_type> operating tables <END> on the <START:location_id> third <END> <START:location_type> floor <END> count all <START:item_type> items <END> on the <START:location_id> third <END> <START:location_type> floor <END> how many <START:item_type> beds <END> are in <START:location_type> room <END> <START:location_id> 2 <END> The NameFinderME.train(.) method takes a string parameter type . What is the use of this parameter? And, how can I train a model for multiple entities (e.g.