Difference between revisions of "Semantic Role Labeling with CRFs"
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==CRF Model== | ==CRF Model== | ||
The CRF was defined over the tree structure of the sentence as:<br> | The CRF was defined over the tree structure of the sentence as:<br> | ||
| − | <math> | + | [[File:crf_coh.jpg]] |
| − | + | ||
| + | where <math>C</math> | ||
==Results for Rating Prediction== | ==Results for Rating Prediction== | ||
[[File:Results_youtube.jpg]] | [[File:Results_youtube.jpg]] | ||
Revision as of 12:59, 1 October 2011
Contents
Citation
Trevor Cohn, Philip Blunsom, "Semantic Role Labeling with Conditional Random Fields", CoNLL 2005
Online version
Introduction
This paper aims at Semantic Role Labeling or SRL of sentences using Conditional Random Fields. This was the first attempt of solving the problem of SRL using CRF. The authors defined CRF over the tree structure of the syntactic parse tree of the sentence, rather than defining it on the linear sentence structure as is usually done for the tasks of Named Entity Recognition or Part-of-Speech tagging. The motivation behind this came from the very nature of semantic role labeling which is the task of labeling phrases with their semantic labels with respect to a particular constituent of the sentence, the predicate or the verb. The authors conjectured that for this reason, modeling linear chain CRF was not intuitive for SRL. The problem of SRL is usually broken into two parts: identifying candidate phrases for assigning semantic roles, and predicting the semantic role to be assigned to the identified phrase. The approach in this paper does both these things in a single pass over the syntactic tree structure.
Dataset Used
The dataset used was the Propbank corpus, which is the Penn Treebank corpus with semantic role annotation.
CRF Model
The CRF was defined over the tree structure of the sentence as:
where
Results for Rating Prediction
