Hello, Babelfy is not open source software. DBpedia Spotlight performs Named Entity Disambiguation (APL 2.0), UKB (GPL) does WSD and obtains very good results, and the IMS system is available for download. There will be others, I am sure, but just talking off the top of my head.
HTH R On Tue, Feb 27, 2018 at 9:22 PM, Cristian Petroaca <cristian.petro...@gmail.com> wrote: > I agree with you. WSD should be included in OpenNLP once it has a > reasonably good performance. > On the other hand, I have seen few libraries or APIs doing WSD and almost > none doing it right. That may be indicative of how hard the problem is. > > The only promising api I found is Babelfy : http://babelfy.org/about. It > uses a graph based model based on their BabelNet Knowledge base in order to > predict word senses. I think it's based on this paper: > http://www.aclweb.org/anthology/Q14-1019. Any thoughts on this? > > On Sat, Feb 24, 2018 at 7:49 PM, Anthony Beylerian < > anthony.beyler...@gmail.com> wrote: > >> Hey Cristian, >> >> We have tried different approaches such as: >> >> - Lesk (original) [1] >> - Most frequent sense from the data (MFS) >> - Extended Lesk (with different scoring functions) >> - It makes sense (IMS) [2] >> - A sense clustering approach (I don't immediately recall the reference) >> >> Lesk and MFS are meant to be used as baselines for evaluation purpose only. >> The extended version of Lesk is an effort to improve the original, through >> additional information from semantic relationships. >> Although it's not very accurate, it could be useful since it is an >> unsupervised method (no need for large training data). >> However, there were some caveats, as both approaches need to pre-load >> dictionaries as well as score a semantic graph from WordNet at runtime. >> >> IMS is a supervised method which we were hoping to mainly use, since it >> scored around 80% accuracy on SemEval, however that is only for the >> coarse-grained case. However, in reality words have various degrees of >> polysemy, and when tested in the fine-grained case the results were much >> lower. >> We have also experimented with a simple clustering approach but the >> improvements were not considerable as far as I remember. >> >> I just checked the latest results on Semeval2015 [3] and they look a bit >> improved on the fine-grained case ~65% F1. >> However, in some particular domains it looks like the accuracy increases, >> so it could depend on the use case. >> >> On the other hand, there could be some more recent studies that could yield >> better results, but that would need some more investigation. >> >> There are also some other issues such as lack of direct multi-lingual >> support from WordNet, missing sense definitions etc. >> We were also still looking for a better source of sense definitions back >> then. >> In any case, I believe it would be better to have higher performance before >> putting this in the official distribution, however that highly depends on >> the team. >> Otherwise, different parts of the code just need some simple refactoring as >> well. >> >> Best, >> >> Anthony >> >> [1] : M. Lesk, Automatic sense disambiguation using machine readable >> dictionaries >> [2] : https://www.comp.nus.edu.sg/~nght/pubs/ims.pdf >> [3] : http://alt.qcri.org/semeval2015/task13/index.php?id=results >> >> On Wed, Feb 21, 2018 at 5:26 AM, Cristian Petroaca < >> cristian.petro...@gmail.com> wrote: >> >> > Hi Anthony, >> > >> > I'd be interested to discuss this further. >> > What are the wsd methods used? Any links to papers? >> > How does the module perform when being evaluated against Senseval? >> > >> > How much work do you think it's necessary in order to have a functioning >> > WSD module in the context of OpenNLP? >> > >> > Thanks, >> > Cristian >> > >> > >> > >> > On Tue, Feb 20, 2018 at 8:09 AM, Anthony Beylerian < >> > anthony.beyler...@gmail.com> wrote: >> > >> >> Hi Cristian, >> >> >> >> Thank you for your interest. >> >> >> >> The WSD module is currently experimental, so as far as I am aware there >> >> is no timeline for it. >> >> >> >> You can find the sandboxed version here: >> >> https://github.com/apache/opennlp-sandbox/tree/master/opennlp-wsd >> >> >> >> I personally didn't have the time to revisit this for a while and there >> >> are still some details to work out. >> >> But if you are really interested, you are welcome to discuss and >> >> contribute. >> >> I will assist as much as possible. >> >> >> >> Best, >> >> >> >> Anthony >> >> >> >> On Sun, Feb 18, 2018 at 5:52 AM, Cristian Petroaca < >> >> cristian.petro...@gmail.com> wrote: >> >> >> >>> Hi, >> >>> >> >>> I'm interested in word sense disambiguation (particularly based on >> >>> Wordnet). I noticed that the latest OpenNLP version doesn't have any >> but >> >>> I >> >>> remember that a couple of years ago there was somebody working on >> >>> implementing it. Why isn't it in the official OpenNLP jar? Is there a >> >>> timeline for adding it? >> >>> >> >>> Thanks, >> >>> Cristian >> >>> >> >> >> >> >> > >>
