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Looking at SyntaxNet from Google. If there are other candidates worth looking at - please kindly let me know. https://github.com/tensorflow/models/tree/master/

Looking at SyntaxNet from Google. If there are other candidates worth looking at - please kindly let me know. https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/tree/master/syntaxnet

gtani 9y ago
Depends on the corpus. If your problem fits a conLL task, you can read lots of papers about it. If you can build on an existing wikipedia entity/relation graph, dictionary, gazzette that's a big boost. For academic research papers, look at citations for your input stream then SVM tf-idf bigrams. If sentiment/quality analysis, that's another tack
- dartwing 9y ago
  Right now I'm hoping to extract entities and relations between them to use as facts for relevant domain specific search and validation. Reading up the articles, YCombinator included: https://blog.ycombinator.com/how-to-get-into-natural-language-processing/ https://blog.ycombinator.com/how-to-get-into-natural-languag...
  - gtani 9y ago
    I think these PIs at UIUC communicate clearly about all the processes necessary (Han wrote a good text on data mining, but probably outdated from 2011): http://xren7.web.engr.illinois.edu/www17-StructNet-part1.pdf http://xren7.web.engr.illinois.edu/www17-StructNet-part1.pdf http://xren7.web.engr.illinois.edu/cikm16-profile.pdf http://xren7.web.engr.illinois.edu/cikm16-profile.pdf Also solr/lucene/elastic indexes are good tools for filtering your inputs and deciding what the unit doc will be (sentence, paragraph, numbered section of research paper etc
    - dartwing 9y ago
      Thank you! Reading through the papers.
  - gauravnsit 9y ago
    I am also trying to find relations from textual entities, could you suggest any good paper or book to read and good python library to apply this.
    - dartwing 9y ago
      I am starting to play around with: https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/tree/master/syntaxnet I expect that I will be able to match capabilities of this service and customize it to my needs: https://cloud.google.com/natural-language/ https://cloud.google.com/natural-language/

gtani 9y ago

Depends on the corpus. If your problem fits a conLL task, you can read lots of papers about it. If you can build on an existing wikipedia entity/relation graph, dictionary, gazzette that's a big boost. For academic research papers, look at citations for your input stream then SVM tf-idf bigrams. If sentiment/quality analysis, that's another tack

dartwing 9y ago
Right now I'm hoping to extract entities and relations between them to use as facts for relevant domain specific search and validation. Reading up the articles, YCombinator included: https://blog.ycombinator.com/how-to-get-into-natural-language-processing/ https://blog.ycombinator.com/how-to-get-into-natural-languag...
- gtani 9y ago
  I think these PIs at UIUC communicate clearly about all the processes necessary (Han wrote a good text on data mining, but probably outdated from 2011): http://xren7.web.engr.illinois.edu/www17-StructNet-part1.pdf http://xren7.web.engr.illinois.edu/www17-StructNet-part1.pdf http://xren7.web.engr.illinois.edu/cikm16-profile.pdf http://xren7.web.engr.illinois.edu/cikm16-profile.pdf Also solr/lucene/elastic indexes are good tools for filtering your inputs and deciding what the unit doc will be (sentence, paragraph, numbered section of research paper etc
  - dartwing 9y ago
    Thank you! Reading through the papers.
- gauravnsit 9y ago
  I am also trying to find relations from textual entities, could you suggest any good paper or book to read and good python library to apply this.
  - dartwing 9y ago
    I am starting to play around with: https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/tree/master/syntaxnet I expect that I will be able to match capabilities of this service and customize it to my needs: https://cloud.google.com/natural-language/ https://cloud.google.com/natural-language/