How to Extract SVO using NLP in java, i am new in nlp.i am currently using opennlp. but how to do in java with a perticular in java sentence.
LexicalizedParser lp = **new LexicalizedParser("englishPCFG.ser.gz");**
String[] sent = { "This", "is", "an", "easy", "sentence", "." };
Tree parse = (Tree) lp.apply(Arrays.asList(sent));
parse.pennPrint();
System.out.println();
TreePrint tp = new TreePrint("penn,typedDependenciesCollapsed");
tp.print(parse);
getting an compilation error at
new LexicalizedParser("englishPCFG.ser.gz");**
The constructor LexicalizedParser(String) is undefined
it seems as if you are using new version of Stanford NLP parser.
in new version of this parser constructors are not used to allocate memory, instead we are having dedicated functions . you can use :
LexicalizedParser lp = LexicalizedParser.loadModel("englishPCFG.ser.gz");
you can use various overloads of this API.
Stanford documentation for various overloads of loadModel
This is code from the Stanford dependency parser, not from OpenNLP. Follow the example given in ParserDemo.java (and/or ParserDemo2.java) that's included in the stanford-parser directory and make sure that your demo code and the stanford-parser.jar in your classpath are from the same version of the parser. I suspect you are using a more recent version of the parser with older demo code.
You can use Stanford CoreNLP. Check answer here for "rough algorithm" how to get subject-predicate-object from a sentence.
You can use reverb. Check answer here for "reVerb" how to get information extraction from a sentence
Related
I have already successfully parsed sentences to get dependency information using stanford parser (version 3.9.1(run it in IDE Eclipse）) with command "TypedDependencies", but how could I get depnedency information about a single word( it's parent, siblings and children)? I have searched javadoc, it seems Class semanticGraph is used to do this job, but it need a IndexedWord type as input, how do I get IndexedWord? Do you have any simple samples?
You can create a SemanticGraph from a List of TypedDependencies and then you can use the methods getChildren(IndexedWord iw), getParent(IndexedWord iw), and getSiblings(IndexedWord iw). (See the javadoc of SemanticGraph).
To get the IndexedWord of a specific word, you can, for example, use the SemanticGraph method getNodeByIndex(int i), which will return the IndexNode of the i-th token in a sentence.
I'm trying to use Stanford CoreNLP for French texts.
I have two questions:
I want to know if french lemmatization is available with Core NLP?
In some cases the output dependencies do not make sense for example for the sentence "Le chat mange la souris" (the cat is eating the mouse) there is a problem in the token "mange" which is typed as adj and not verb, for that it's not considered as the root of sentence.
But when I use the plurial "Les chats mangent la souris" it's correct.
Any help would be appreciated!
At this time we do not have a French language lemmatizer.
We will be releasing a new French dependencies model soon with our official 3.7.0 release. I am curious though, how are you generating dependencies, with the "parse" annotator or "depparse" annotator?
Thanks for your response.
I use the following configuration for the parse and depparse methods:
StanfordCoreNLP pipeline = new StanfordCoreNLP(
PropertiesUtils.asProperties(
"annotators", "tokenize, ssplit, pos, depparse, parse",
"tokenize.language", "fr",
"pos.model", "edu/stanford/nlp/models/pos- tagger/french/french.tagger",
"parse.model", "edu/stanford/nlp/models/lexparser/frenchFactored.ser.gz",
"depparse.model", "edu/stanford/nlp/models/parser/nndep/UD_French.gz"));
I have been trying to use the Stanford CoreNLP API included in the 2015-12-09 release. I start the server using:
java -mx5g -cp "./*" edu.stanford.nlp.pipelinStanfordCoreNLPServer
The server works in general, but fails for some setnences including the following:
"Aside from her specifically regional accent, she reveals by the use of the triad, ``irritable, tense, depressed, a certain pedantic itemization that indicates she has some familiarity with literary or scientific language ( i.e., she must have had at least a highschool education ) , and she is telling a story she has mentally rehearsed some time before."
I end up with a result that starts with :
{"sentences":[{"index":0,"parse":"SENTENCE_SKIPPED_OR_UNPARSABLE","basic-dependencies":
I would greatly appriciate some help in setting this up - am I not including some annotators in the nlp pipeline.
This same sentence works at http://corenlp.run/
If you're looking for a dependency parse (like that in corenlp.run), you should look at the basic-dependencies field rather than the parse field. If you want a constituency parse, you should include the parse annotator in the list of annotators you are sending to the server. By default, the server does not include the parser annotator, as it's relatively slow.
Using Stanford CoreNLP, I am trying to parse text using the neural nets dependency parser. It runs really fast (that's why I want to use this and not the LexicalizedParser), and produces high-quality dependency relations. I am also interested in retrieving the parse trees (Penn-tree style) from that too. So, given the GrammaticalStructure, I am getting the root of that (using root()), and then trying to print it out using the toOneLineString() method. However, root() returns the root node of the tree, with an empty/null list of children. I couldn't find anything on this in the instructions or FAQs.
GrammaticalStructure gs = parser.predict(tagged);
// Print typed dependencies
System.err.println(gs);
// get the tree and print it out in the parenthesised form
TreeGraphNode tree = gs.root();
System.err.println(tree.toOneLineString());
The output of this is:
ROOT-0{CharacterOffsetBeginAnnotation=-1, CharacterOffsetEndAnnotation=-1, PartOfSpeechAnnotation=null, TextAnnotation=ROOT}Typed Dependencies:
[nsubj(tell-5, I-1), aux(tell-5, can-2), advmod(always-4, almost-3), advmod(tell-5, always-4), root(ROOT-0, tell-5), advmod(use-8, when-6), nsubj(use-8, movies-7), advcl(tell-5, use-8), amod(dinosaurs-10, fake-9), dobj(use-8, dinosaurs-10), punct(tell-5, .-11)]
ROOT-0
How can I get the parse tree too?
Figured I can use the Shift-Reduce constituency parser made available by Stanford. It's very fast and the results are comparable.
I'm using the Stanford's CoreNLP Named Entity Recognizer (NER) and Part-of-Speech (POS) tagger in my application. The problem is that my code tokenizes the text beforehand and then I need to NER and POS tag each token. However I was only able to find out how to do that using the command line options but not programmatically.
Can someone please tell me how programmatically can I NER and POS tag pretokenized text using Stanford's CoreNLP?
Edit:
I'm actually using the individual NER and POS instructions. So my code was written as instructed in the tutorials given in the Stanford's NER and POS packages. But I have CoreNLP in my classpath. So I have the CoreNLP in my classpath but using the tutorials in the NER and POS packages.
Edit:
I just found that there are instructions as how one can set the properties for CoreNLP here http://nlp.stanford.edu/software/corenlp.shtml but I wish if there was a quick way to do what I want with Stanford NER and POS taggers so I don't have to recode everything!
If you set the property:
tokenize.whitespace = true
then the CoreNLP pipeline will tokenize on whitespace rather than the default PTB tokenization. You may also want to set:
ssplit.eolonly = true
so that you only split sentences on newline characters.
To programmatically run a classifier over a list of tokens that you've already gotten via some other means, without a kludge like pasting them together with whitespace and then tokenizing again, you can use the Sentence.toCoreLabelList method:
String[] token_strs = {"John", "met", "Amy", "in", "Los", "Angeles"};
List<CoreLabel> tokens = edu.stanford.nlp.ling.Sentence.toCoreLabelList(token_strs);
for (CoreLabel cl : classifier.classifySentence(tokens)) {
System.out.println(cl.toShorterString());
}
Output:
[Value=John Text=John Position=0 Answer=PERSON Shape=Xxxx DistSim=463]
[Value=met Text=met Position=1 Answer=O Shape=xxxk DistSim=476]
[Value=Amy Text=Amy Position=2 Answer=PERSON Shape=Xxx DistSim=396]
[Value=in Text=in Position=3 Answer=O Shape=xxk DistSim=510]
[Value=Los Text=Los Position=4 Answer=LOCATION Shape=Xxx DistSim=449]
[Value=Angeles Text=Angeles Position=5 Answer=LOCATION Shape=Xxxxx DistSim=199]