---
title: "Representing Sentence Structure in Hidden Markov Models for Information Extraction"
authors:
  - "Soumya Ray"
  - "Mark Craven"
doi: ""
url: "https://ijcai.org/proceedings/2001/167"
pdf: "https://ijcai.org/proceedings/2001/0167.pdf"
venue: "Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence"
year: 2001
pages: "1273-1278"
track: ""
keywords:
---

# Representing Sentence Structure in Hidden Markov Models for Information Extraction

Soumya Ray, Mark Craven

*Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence*, pages 1273-1278.

## Abstract

We study the application of Hidden Markov Models (HMMs) to learning information extractors for -ary relations from free text. We propose an approach to representing the grammatical structure of sentences in the states of the model. We also investigate using an objective function during HMM training which maximizes the ability of the learned models to identify the phrases of interest. We evaluate our methods by deriving extractors for two binary relations in biomedical domains. Our experiments indicate that our approach learns more accurate models than several baseline approaches.