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coreNlp | Extensions for and tools to work with CoreNlp | Natural Language Processing library

by jconwell Java Version: Current License: Apache-2.0

by jconwell Java Version: Current License: Apache-2.0

kandi X-RAY | coreNlp Summary

coreNlp is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. coreNlp has no bugs, it has build file available, it has a Permissive License and it has low support. However coreNlp has 4 vulnerabilities. You can download it from GitHub.

CoreNlp doesnt have stopword identification built in, so I wrote an extension to its analytics pipeline (called Annotators) to check if a token’s word and lemma value are stopwords. By default, the StopwordAnnotator uses the built in Lucene stopword list, but you have to option to pass in a custom list of stopwords for it to use instead. You can also specify if the StopwordAnnotator should check the lemma of the token against the stopword list or not. For examples of how to use the StopwordAnnotator, takea look at StopwordAnnotatorTest.java.

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kandi-support Support

coreNlp has a low active ecosystem.
It has 28 star(s) with 13 fork(s). There are 5 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 2 have been closed. On average issues are closed in 68 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of coreNlp is current.

coreNlp Support

Best in #Natural Language Processing

Average in #Natural Language Processing

coreNlp Support

Best in #Natural Language Processing

Average in #Natural Language Processing

quality kandi Quality

coreNlp has 0 bugs and 16 code smells.

coreNlp Quality

Best in #Natural Language Processing

Average in #Natural Language Processing

coreNlp Quality

Best in #Natural Language Processing

Average in #Natural Language Processing

security Security

coreNlp has 4 vulnerability issues reported (2 critical, 2 high, 0 medium, 0 low).
coreNlp code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

coreNlp Security

Best in #Natural Language Processing

Average in #Natural Language Processing

coreNlp Security

Best in #Natural Language Processing

Average in #Natural Language Processing

license License

coreNlp is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

coreNlp License

Best in #Natural Language Processing

Average in #Natural Language Processing

coreNlp License

Best in #Natural Language Processing

Average in #Natural Language Processing

build Reuse

coreNlp releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
coreNlp saves you 197 person hours of effort in developing the same functionality from scratch.
It has 485 lines of code, 30 functions and 5 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

coreNlp Reuse

Best in #Natural Language Processing

Average in #Natural Language Processing

coreNlp Reuse

Best in #Natural Language Processing

Average in #Natural Language Processing

Top functions reviewed by kandi - BETA

kandi has reviewed coreNlp and discovered the below as its top functions. This is intended to give you an instant insight into coreNlp implemented functionality, and help decide if they suit your requirements.

Constructs the properties for the core nlp .

Overloads the token annotation .

Replies the types that require the annotation .

Gets stopword list from string .

Returns the type of the booleanPair .

Builds the NLP analyzer .

Return the set of StopwordAnnotator .

Options for named entity .

Default configuration for named entities with core preference analysis .

Get tokenization only .

Get all kandi verified functions for this library.

Get all kandi verified functions for this library.

coreNlp Key Features

Extensions for and tools to work with CoreNlp

coreNlp Examples and Code Snippets

Community Discussions

Trending Discussions on coreNlp

Stanford CoreNLP - Unknown variable WORKDAY
NLP Pipeline, DKPro, Ruta - Missing Descriptor Error
Meaning of output/training status of 256 in Stanford NLP NER?
Is there a way to use external libraries in IntelliJ without downloading their .jars?
Stanford Core NLP Tree Parser Sentence Limits wrong - suggestions?
How can I iterate token attributes with coreference results in CoreNLP?
Stanford CoreNLP: Java can't find or load main class / java.ClassNotFoundException
Issue in creating Semgrex patterns with relation names containing ":" colon
Access server running on docker container
Pattern matching with tregex in Stanzas Corenlp implementation doesn't seem to finde the right subtrees

Trending Discussions on coreNlp

QUESTION

Stanford CoreNLP - Unknown variable WORKDAY

Asked 2021-Nov-20 at 19:28

I am processing some documents and I am getting many WORKDAY messages as seen below. There's a similar issue posted here for WEEKDAY. Does anyone know how to deal with this message. I am running corenlp in a Java server on Windows and accessing it using Juypyter Notebook and Python code.

[pool-2-thread-2] INFO edu.stanford.nlp.ling.tokensregex.types.Expressions - Unknown variable: WORKDAY
[pool-2-thread-2] INFO edu.stanford.nlp.ling.tokensregex.types.Expressions - Unknown variable: WORKDAY
[pool-2-thread-2] INFO edu.stanford.nlp.ling.tokensregex.types.Expressions - Unknown variable: WORKDAY
[pool-1-thread-7] WARN CoreNLP - java.util.concurrent.ExecutionException: java.lang.RuntimeException: Error making document

ANSWER

Answered 2021-Nov-20 at 19:28

This is an error in the current SUTime rules file (and it's actually been there for quite a few versions). If you want to fix it immediately, you can do the following. Or we'll fix it in the next release. These are Unix commands, but the same thing will work elsewhere except for how you refer to and create folders.

Find this line in sutime/english.sutime.txt and delete it. Save the file.

{ (/workday|work day|business hours/) => WORKDAY }

Then move the file to the right location for replacing in the jar file, and then replace it in the jar file. In the root directory of the CoreNLP distribution do the following (assuming you don't already have an edu file/folder in that directory):

mkdir -p edu/stanford/nlp/models/sutime
cp sutime/english.sutime.txt edu/stanford/nlp/models/sutime
jar -uf stanford-corenlp-4.2.0-models.jar edu/stanford/nlp/models/sutime/english.sutime.txt
rm -rf edu

Source https://stackoverflow.com/questions/69955279

QUESTION

NLP Pipeline, DKPro, Ruta - Missing Descriptor Error

Asked 2021-Aug-15 at 10:09

I am trying to run a RUTA script with an analysis pipeline.

I add my script to the pipeline like so createEngineDescription(RutaEngine.class, RutaEngine.PARAM_MAIN_SCRIPT, "mypath/myScript.ruta)

My ruta script file contains this:

IMPORT PACKAGE de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.pos
    FROM desc.type.POS AS pos;
IMPORT de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Lemma
    FROM desc.type.LexicalUnits;
IMPORT de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Token
    FROM desc.type.LexicalUnits_customized AS token;
IMPORT de.tudarmstadt.ukp.dkpro.core.api.syntax.type.dependency.Dependency
    FROM desc.type.Dependency AS dep;
IMPORT de.tudarmstadt.ukp.dkpro.core.type.ReadabilityScore
    FROM desc.type.ReadabilityScore;
IMPORT de.tudarmstadt.ukp.dkpro.core.api.metadata.type.TagsetDescription
    FROM desc.type.metadata;

UIMAFIT de.tudarmstadt.ukp.dkpro.core.opennlp.OpenNlpSegmenter;
UIMAFIT de.tudarmstadt.ukp.dkpro.core.opennlp.OpenNlpPosTagger;
UIMAFIT de.tudarmstadt.ukp.dkpro.core.corenlp.CoreNlpLemmatizer;
UIMAFIT de.tudarmstadt.ukp.dkpro.core.maltparser.MaltParser;
UIMAFIT de.tudarmstadt.ukp.dkpro.core.readability.ReadabilityAnnotator;

uima.tcas.DocumentAnnotation{-CONTAINS(pos.POS)} -> {
    uima.tcas.DocumentAnnotation{-> SETFEATURE("language", "en")};
EXEC(OpenNlpSegmenter);
EXEC(OpenNlpPosTagger);  
EXEC(CoreNlpLemmatizer);
EXEC(MaltParser); 
EXEC(ReadabilityAnnotator);
};

This generates the error -> Annotation exception: Initialization of annotator class "org.apache.uima.ruta.engine.RutaEngine" failed. (Descriptor: unknown)

Do I need a descriptor? This answer How to create pipeline of java nlp and ruta scripts? suggests to me that its not required but perhaps I am misunderstanding what is required. If it is needed then how do I add it?

I am using uimafit-core:2.5.+ and org.apache.uima:ruta-core:2.8.1

Scanning for other solutions I also tried this

AnalysisEngine aae = createEngine(RutaEngine.class,
            RutaEngine.PARAM_MAIN_SCRIPT, "myscript.ruta",
            RutaEngine.PARAM_SCRIPT_PATHS, new String[] { "src/main/resources/ruta" },
            RutaEngine.PARAM_ADDITIONAL_EXTENSIONS, new String[] {
                    BooleanOperationsExtension.class.getName(),
                    StringOperationsExtension.class.getName()});

but with no improvement. I get the same error.

ANSWER

Answered 2021-Aug-15 at 10:09

I solved the problem. This error was being thrown simply because the script could not be found and I had to change this line from: RutaEngine.PARAM_MAIN_SCRIPT, "myscript.ruta" to: RutaEngine.PARAM_MAIN_SCRIPT, "myscript"

However, I did a few other things before this that may have contributed to the solution so I am listing them here:

I added the ruta nature to my eclipse project
I moved the myscript from resources to a script package

Source https://stackoverflow.com/questions/68784592

QUESTION

How can I iterate token attributes with coreference results in CoreNLP?

Asked 2021-Jan-07 at 22:46

I am looking for a way to extract and merge annotation results from CoreNLP. To specify,

import stanza
import os
from stanza.server import CoreNLPClient
corenlp_dir = '/Users/fatih/stanford-corenlp-4.2.0/'
os.environ['CORENLP_HOME'] = corenlp_dir

client = CoreNLPClient(
    annotators=['tokenize','ssplit', 'pos', 'lemma', 'ner', 'coref'], 
    memory='4G', 
    endpoint='http://localhost:9001',
    be_quiet=True)

text = "Barack Obama was born in Hawaii.  He is the president. Obama was elected in 2008."

doc = client.annotate(text)

for x in doc.corefChain:
    for y in x.mention:
        print(y.animacy)
        
ANIMATE
ANIMATE
ANIMATE

I'd like to merge these results with the ones from following code:

for i, sent in enumerate(document.sentence):
    print("[Sentence {}]".format(i+1))
    for t in sent.token:
        print("{:12s}\t{:12s}\t{:6s}\t{}".format(t.word, t.lemma, t.pos, t.ner))
    print("")

Barack          Barack          NNP     PERSON
Obama           Obama           NNP     PERSON
was             be              VBD     O
born            bear            VBN     O
in              in              IN      O
Hawaii          Hawaii          NNP     STATE_OR_PROVINCE
.               .               .       O

[Sentence 2]
He              he              PRP     O
is              be              VBZ     O
the             the             DT      O
president       president       NN      TITLE
.               .               .       O

[Sentence 3]
Obama           Obama           NNP     PERSON
was             be              VBD     O
elected         elect           VBN     O
in              in              IN      O
2008            2008            CD      DATE
.               .               .       O

Since annotations are stored in different object, I cannot iterate over the two different object and get the results for related items.

Is there a way out?

Thanks.

ANSWER

Answered 2021-Jan-07 at 22:46

The coref chains have a sentenceIndex and a beginIndex which should correlate to the position in the sentence. You can use this to correlate the two.

https://github.com/stanfordnlp/stanza/blob/f0338f891a03e242c7e11e440dec6e191d54ab77/doc/CoreNLP.proto#L319

Edit: quick and dirty change to your example code:

from collections import defaultdict
from stanza.server import CoreNLPClient

client = CoreNLPClient(
    annotators=['tokenize','ssplit', 'pos', 'lemma', 'ner', 'coref'],
    be_quiet=False)

text = "Barack Obama was born in Hawaii.  In 2008 he became the president."

doc = client.annotate(text)

animacy = defaultdict(dict)
for x in doc.corefChain:
    for y in x.mention:
        print(y.animacy)
        for i in range(y.beginIndex, y.endIndex):
            animacy[y.sentenceIndex][i] = True
            print(y.sentenceIndex, i)

for sent_idx, sent in enumerate(doc.sentence):
    print("[Sentence {}]".format(sent_idx+1))
    for t_idx, token in enumerate(sent.token):
        animate = animacy[sent_idx].get(t_idx, False)
        print("{:12s}\t{:12s}\t{:6s}\t{:20s}\t{}".format(token.word, token.lemma, token.pos, token.ner, animate))
    print("")

Source https://stackoverflow.com/questions/65542790

QUESTION

Access server running on docker container

Asked 2020-Oct-07 at 08:08

I am running the StanfordCoreNLP server through my docker container. Now I want to access it through my python script.

Github repo I'm trying to run: https://github.com/swisscom/ai-research-keyphrase-extraction

I ran the command which gave me the following output:

docker run -v /home/goorulabs/NarrativeArcse-extraction/sent2vec/torontobooks_unigrams.bin:/sent2vec/pretrained_model.bin -it keyphrase-extraction
/app # cd /stanford-corenlp/
/stanford-corenlp # java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -preload tokenize,ssplit,pos -status_port 9000 -port 9000 -time
out 15000 &
/stanford-corenlp # [main] INFO CoreNLP - --- StanfordCoreNLPServer#main() called ---
[main] INFO CoreNLP - setting default constituency parser
[main] INFO CoreNLP - warning: cannot find edu/stanford/nlp/models/srparser/englishSR.ser.gz
[main] INFO CoreNLP - using: edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz instead
[main] INFO CoreNLP - to use shift reduce parser download English models jar from:
[main] INFO CoreNLP - http://stanfordnlp.github.io/CoreNLP/download.html
[main] INFO CoreNLP -     Threads: 4
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator tokenize
[main] INFO edu.stanford.nlp.pipeline.TokenizerAnnotator - No tokenizer type provided. Defaulting to PTBTokenizer.
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator ssplit
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator pos
[main] INFO edu.stanford.nlp.tagger.maxent.MaxentTagger - Loading POS tagger from edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger ... done [1.2 sec].
[main] INFO CoreNLP - Starting server...
[main] INFO CoreNLP - StanfordCoreNLPServer listening at /0.0.0.0:9000

Now I tried to access it by specifying the host and the port in the config.ini file but got the following error.

Traceback (most recent call last):
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connection.py", line 160, in _new_conn
    (self._dns_host, self.port), self.timeout, **extra_kw
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/util/connection.py", line 84, in create_connection
    raise err
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/util/connection.py", line 74, in create_connection
    sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connectionpool.py", line 677, in urlopen
    chunked=chunked,
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connectionpool.py", line 392, in _make_request
    conn.request(method, url, **httplib_request_kw)
  File "/usr/lib/python3.6/http/client.py", line 1264, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/usr/lib/python3.6/http/client.py", line 1310, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/usr/lib/python3.6/http/client.py", line 1259, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/lib/python3.6/http/client.py", line 1038, in _send_output
    self.send(msg)
  File "/usr/lib/python3.6/http/client.py", line 976, in send
    self.connect()
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connection.py", line 187, in connect
    conn = self._new_conn()
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connection.py", line 172, in _new_conn
    self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7fb5222fab70>: Failed to establish a new connection: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/requests/adapters.py", line 449, in send
    timeout=timeout
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/connectionpool.py", line 727, in urlopen
    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/urllib3/util/retry.py", line 439, in increment
    raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='127.0.0.1', port=9000): Max retries exceeded with url: /?properties=%7B%22outputFormat%22%3A+%22json%22%2C+%22annotators%22%3A+%22tokenize%2Cssplit%2Cpos%22%7D (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fb5222fab70>: Failed to establish a new connection: [Errno 111] Connection refused',))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test.py", line 7, in <module>
    kp1 = launch.extract_keyphrases(embedding_distributor, pos_tagger, raw_text, 5,'en')
  File "/home/goorulabs/NarrativeArc/ai-research-keyphrase-extraction/launch.py", line 27, in extract_keyphrases
    tagged = ptagger.pos_tag_raw_text(raw_text)
  File "/home/goorulabs/NarrativeArc/ai-research-keyphrase-extraction/swisscom_ai/research_keyphrase/preprocessing/postagging.py", line 215, in pos_tag_raw_text
    tagged_text = list(raw_tag_text())
  File "/home/goorulabs/NarrativeArc/ai-research-keyphrase-extraction/swisscom_ai/research_keyphrase/preprocessing/postagging.py", line 211, in raw_tag_text
    tagged_data = self.parser.api_call(text, properties=properties)
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/nltk/parse/corenlp.py", line 247, in api_call
    timeout=timeout,
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/requests/sessions.py", line 578, in post
    return self.request('POST', url, data=data, json=json, **kwargs)
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/requests/sessions.py", line 530, in request
    resp = self.send(prep, **send_kwargs)
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/requests/sessions.py", line 643, in send
    r = adapter.send(request, **kwargs)
  File "/home/goorulabs/NarrativeArc/narrativearc/lib/python3.6/site-packages/requests/adapters.py", line 516, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPConnectionPool(host='127.0.0.1', port=9000): Max retries exceeded with url: /?properties=%7B%22outputFormat%22%3A+%22json%22%2C+%22annotators%22%3A+%22tokenize%2Cssplit%2Cpos%22%7D (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fb5222fab70>: Failed to establish a new connection: [Errno 111] Connection refused',))

The docker ps shows a blank column for PORTS

ANSWER

Answered 2020-Oct-07 at 08:08

As seen in the log, your service is listening to port 9000 inside the container. However, from outside you need further information to be able to access it. Two pieces of information that you need:

The IP address of the container
The external port that docker exports this 9000 to the outside (by default docker does not export locally open ports).

To get the IP address you need to use docker inspect, for example via

docker inspect keyphrase-extraction | grep IPAddress

This should give you a line showing the IP address that you can use from your local host machine to address docker.

Regarding the port, it needs to be explicitly exposed. A quick look at the Dockerfile from the repo doesn't show any EXPOSE docker commands, so the port 9000 doesn't seem to be exposed by default.

You can expose it by adding -p9000:9000 to your command (format is -p HOST_PORT:CONTAINER_PORT) like this:

docker run -p9000:9000 -v /home/goorulabs/NarrativeArcse-extraction/sent2vec/torontobooks_unigrams.bin:/sent2vec/pretrained_model.bin -it keyphrase-extraction

After you do all of this, you can test whether your port has been successfully exposed for example by using:

telnet 172.17.0.1 9000

assuming that the above is the IP you get from the first step.

Source https://stackoverflow.com/questions/64238613

QUESTION

Pattern matching with tregex in Stanzas Corenlp implementation doesn't seem to finde the right subtrees

Asked 2020-Sep-22 at 22:01

I am relatively new to NLP and at the moment I'm trying to extract different phrase scructures in german texts. For that I'm using the Stanford corenlp implementation of stanza with the tregex feature for pattern machting in trees.

So far I didn't have any problem an I was able to match simple patterns like "NPs" or "S > CS". No I'm trying to match S nodes that are immediately dominated either by ROOT or by a CS node that is immediately dominated by ROOT. For that im using the pattern "S > (CS > TOP) | > TOP". But it seems that it doesn't work properly. I'm using the following code:

text = "Peter kommt und Paul geht."    
def linguistic_units(_client, _text, _pattern):
        matches = _client.tregex(_text,_pattern)
        list = matches['sentences']
        print('+++++Tree++++') 
        print(list[0])
        for sentence in matches['sentences']:
            for match_id in sentence:
                print(sentence[match_id]['spanString'])
        return count_units



with CoreNLPClient(properties='./corenlp/StanfordCoreNLP-german.properties', 
                   annotators=['tokenize', 'ssplit', 'pos', 'lemma', 'ner', 'parse', 'depparse', 'coref'],
                   timeout=300000, 
                   be_quiet=True,
                   endpoint='http://localhost:9001', 
                   memory='16G') as client:

      result = linguistic_units(client, text, 'S > (CS > ROOT) | > ROOT'
      print(result)

In the example with the text "Peter kommt und Paul geht" the pattern I'm using should match the two phrases "Peter kommt" and "Paul geht", but it doesn't work. Afterwards I had a look at the tree itselfe and the output of the parser was the following:

constituency parse of first sentence
child {
  child {
    child {
      child {
        child {
          value: "Peter"
        }
        value: "PROPN"
      }
      child {
        child {
          value: "kommt"
        }
        value: "VERB"
      }
      value: "S"
    }
    child {
      child {
        value: "und"
      }
      value: "CCONJ"
    }
    child {
      child {
        child {
          value: "Paul"
        }
        value: "PROPN"
      }
      child {
        child {
          value: "geht"
        }
        value: "VERB"
      }
      value: "S"
    }
    value: "CS"
  }
  child {
    child {
      value: "."
    }
    value: "PUNCT"
  }
  value: "NUR"
}
value: "ROOT"
score: 5466.83349609375

I now suspect that this is due to the ROOT node, since it is the last node of the tree. Should the ROOT node not be at the beginning of the tree? Does anyone know what I am doing wrong?

ANSWER

Answered 2020-Sep-22 at 22:01

A few comments:

1.) Assuming you are using a recent version of CoreNLP (4.0.0+), you need to use the mwt annotator with German. So your annotators list should be tokenize,ssplit,mwt,pos,parse

2.) Here is your sentence in PTB for clarity:

(ROOT
  (NUR
    (CS
      (S (PROPN Peter) (VERB kommt))
      (CCONJ und)
      (S (PROPN Paul) (VERB geht)))))

As you can see the ROOT is the root node of the tree, so your pattern would not match in this sentence. I personally find the PTB format easier to see the tree structure and for writing Tregex patterns off of. You can get that via the json or text output formats (instead of the serialized object). In the client request set output_format="text"

3.) Here is the latest documentation on using the Stanza client: https://stanfordnlp.github.io/stanza/client_properties.html

Source https://stackoverflow.com/questions/64016461

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install coreNlp

You can download it from GitHub.
You can use coreNlp like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the coreNlp component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at:

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