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Semantic role labeling

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Semantic Roles are descriptions of the semantic relation between predicate and its arguments with application in relation extraction , question answering

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Semantic role labeling

  1. 1. Semantic Role Labeling 说明 Sanjay Meena Place : Taipei
  2. 2. Topics Semantic Role Labeling
  3. 3. Semantic Role Labeling • Semantic Roles are descriptions of the semantic relation between predicate and its arguments • Applications: • Question Answering • Information Extraction
  4. 4. Semantic Role Labeling System • Two System versions • Chained Pipeline of Logistic Regression models • Features Based on Dependency Parsing and POS • Deep Learning Based • End to End Supervised Learning
  5. 5. SRL Architectures • SRL Web Service • SRL Data Tagging Process • SRL Model Training Process
  6. 6. SRL Web Service Web Server JSON Web Services ••Multiple Parameters support ••Modular Design. New functionalities can be added with URL rerouting SRL Visualization ••Scala+ AJAX + HTML + Java Scripts SRL System Jar (Java) All information can be made accessible by web service Docker Container User Web Service or SRL Visual page You can see the the links for webpage and web service here : (More details in later slides.) http://wiki.emotibot.com:8090/display/NLP/SRL
  7. 7. SRL Process from Web Request to end result Web Server JSON Web Services ••Multiple Parameters ••Backward Compatible across SRL Versions ••Modular Design. New functionalities can be added with URL rerouting SRL Visualization Web Page ••Scala+ AJAX + HTML + Java Scripts enabled Web page SRL System 1) Word segmentation (NLP) 2) POS Tagging 3) Dependency Parsing 4) SRL Prediction 5) SRL Format Parser + JSON Docker User/module Use Web Service or SRL Web Page 1 2 3.1 3.2 3.3 3.4 3.5 Number Indicate steps in the processing
  8. 8. SRL Data Tagging Process SRL Data Ready for Model training 1) SRL Model 2) POS Model* 3) Dependency Parser Model* 4) Run Data Validation Program (SRL System) Run Validation Fix Mistakes and repeat running program until all data is validated 3) Human Tagging 1) Add/Modify SRL Tags 2) Add/Modify Predicates 3) Fix POS * 4) Fix Dependency Parse* 2) SRL System 1) First Create CONLL Format Sentence 2) Create HIT format Sentence (easier for tagging) 3) Output is TSV format data that can be opened in excel 1) Input : Sentences The whole process is performed again for new data POS Model is not trained anymore as we use it from NLP module . Dependency Parser Model is trained mainly to fix bugs.
  9. 9. Example of Format
  10. 10. Machine Learning Steps in SRL 1. Create Pipeline of linear classifiers consisting of 1) Predicate disambiguation 2) Argument Identification 3) Argument Classification • All Classifiers built using L2 regularized logistic regression . 2. Beam search using the models result to generate a pool of candidates 3. Rerank the candidates using joint learning approach that combines the above 3 linear classifier models and proposition features
  11. 11. SRL Model Training Process Model Evaluation 4. Save a single model 3. Training Pipeline of Classifiers 1) Predicate Disambiguation 2) Argument Identification Module 3) Argument Classification 2. Data Format Conversion 1) Data Format Converter ••Converts from HIT to CONLL Format 2) Data Validator ••Tells us if any data mistakes 1. Input : HIT Data Format
  12. 12. Deep Learning Based • Based on DB-LSTM model which achieved state of the art results • Paper: End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks • Two models used: • RNN Based model to identify predicates in sentence • DB-LSTM Model to extract the SRL Relations • Word embedding's used to represent words trained with vocab ~700000 words using fast-text library and skipgram architecture
  13. 13. Sentence Representation BIO Representation
  14. 14. Network architecture
  15. 15. Features • Total 9 Features : word sequence, predicate, predicate context (5 columns), region mark sequence, label sequence. T
  16. 16. Thank You

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