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

1. Semantic Role Labeling
说明
Sanjay Meena
Place : Taipei

2. Topics
Semantic Role Labeling

3. Semantic Role Labeling
• Semantic Roles are descriptions of the semantic relation between predicate and
its arguments
• Applications:
• Question Answering
• Information Extraction

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. SRL Architectures
• SRL Web Service
• SRL Data Tagging Process
• SRL Model Training Process

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. 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. 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. Example of Format

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. 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. 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. Sentence Representation
BIO Representation

14. Network architecture

15. Features
• Total 9 Features : word sequence, predicate, predicate context (5
columns), region mark sequence, label sequence. T

16. Thank You