Was using StanfordOepnIE for my professor on a research project.
I can successfully extract the triples by using OpenIE annotator from the Standford NLP server.
However, the confidence score was not returned with the requested json as it was shown on the website
https://nlp.stanford.edu/software/openie.html.
Apparently it seemed like that was not being implemented yet by the Stanford people.
Anyone has solution to the problem or have alternative python library that I can to extract both the expected output with its confidence level from the Stanford OpenIE?
The text output has the confidences. We can add the confidences into the json for future versions.
Related
I'm working on entity extraction for one of my projects and came across CoreNLP. The demo works pretty good, but I can't seem to find any documentation on the entitylink/Wikipedia annotator. Anyone have any sources on what techniques and data were used for these models?
This is based off of Angel Chang's Wikidict resource: http://nlp.stanford.edu/pubs/crosswikis.pdf , albeit munged a fair bit to allow it to be loaded into memory.
I am making my own model of Stanford NER which is CRF based, by following conventions given at this link.I want to add Gazettes and following this from same link. I am mentioning all of my Gazettes using this property, gazette=file1.txt;file2.txt and also mentioning useGazettes=true in austen.prop. After making model when I am testing data from my Gazettes then it is not TAGGING correctly. The tag which I mentioned in files in not coming correctly. These are little bit surprising results for me as Stanford NER is not giving them same tag as mentioned in those files.
Is there some limitations of Stanford NER with Gazettes or I am still missing something? If somebody can help me I will be thankful to you.
I would like to use the Stanford CoreNLP library to do co-referencing in Dutch.
My question is, how do I train the CoreNLP to handle Dutch co-referencing resolution?
We've already created a Dutch NER model based on the 'conll2002' set (https://github.com/WillemJan/Stanford_ner_bugreport/raw/master/dutch.gz), but we would also like to use the co-referencing module in the same way.
Look at the class edu.stanford.nlp.scoref.StatisticalCorefTrainer.
The appropriate properties file for English is in:
edu/stanford/nlp/scoref/properties/scoref-train-conll.properties
You may have to get the latest code base from GitHub:
https://github.com/stanfordnlp/CoreNLP
While we are not currently supporting training of the statistical coreference models in the toolkit, I do believe the code for training them is included and it is certainly possible it works right now. I have yet to verify if it is functioning properly.
Please let me know if you need any more assistance. If you encounter bugs I can try to fix them...we would definitely like to get the statistical coreference training operational for future releases!
I'm using the Sandford CoreNLP.NET module and its CRFClassifier to find Named Entities in a document. I am able to get the entities by using classifyWithInlineXML, but does anyone know how to get the entities along with their relevance/confidence scores (0-1)?
Would love an example in C# on how to do this.
Looks like a duplicate of Display Stanford NER confidence score question.
All you need is to rewrite provided sample in C#.
I want to use the Stanford NLP API to parse text and extract temporal expressions. The Core NLP package comes with SUTime, a library for recognizing and normalizing time expressions. Following the example on their site, I have easily found the expressions I want.
However, the online demo has a checkbox for 'include range', which is very useful to me. How can I pass this flag to the library API? I can't seem to find it in their documentation.
After combing through the Java NLP mailing list archives, I found this page which explains the issue. The way to pass options into the TimeAnnotator is to add properties, in this case:
props.setProperty("sutime.includeRange", "true");
I hope this helps someone in the future, maybe even myself :-)