---
title: "Collective Semantic Role Labeling for Tweets with Clustering"
authors:
  - "Xiaohua Liu"
  - "Kuan Li"
  - "Ming Zhou"
  - "Zhongyang Xiong"
doi: "10.5591/978-1-57735-516-8/IJCAI11-307"
url: "https://ijcai.org/proceedings/2011/307"
pdf: "https://ijcai.org/proceedings/2011/0307.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1832-1836"
track: ""
keywords:
---

# Collective Semantic Role Labeling for Tweets with Clustering

Xiaohua Liu, Kuan Li, Ming Zhou, Zhongyang Xiong

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1832-1836. DOI: [10.5591/978-1-57735-516-8/IJCAI11-307](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-307)

## Abstract

As tweets has become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its center role in a wide range of tweet related studies such as fine-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is often too short and informal to provide sufficient information poses a main challenge. To tackle this challenge, we propose a new method to collectively label similar tweets. The underlying idea is to exploit similar tweets to make up for the lack of information in a tweet. Specifically, similar tweets are first grouped together by clustering. Then for each cluster a two-stage labeling is conducted: One labeler conducts SRL to get statistical information, such as the predicate/argument/role triples that occur frequently, from its highly confidently labeled results; then in the second stage, another labeler performs SRL with such statistical information to refine the results. Experimental results on a human annotated dataset show that our approach remarkably improves SRL by 3.1% F1.