Multi-Task Learning in Conditional Random Fields for Chunking in Shallow Semantic Parsing
نویسندگان
چکیده
Alternating Structure Optimization (ASO) is a recently proposed linear Multitask Learning algorithm. Although its effective has been verified in both semi-supervised as well as supervised methods, yet they necessitate taking external resource as a prerequisite. Therefore, feasibility of employing ASO to further improve the performance merely rests on the labeled data on hand proves to be a task deserving close scrutiny. Catering to this challenging while untapped problem, this paper presents a novel application of ASO to the subtask of Shallow Semantic Parsing: Chunking. Our experiments on Chinese Treebank 5.0 present promising result in chunk analysis, and the error rate is reduced by 5.72%, proposing a profound way to further improve the performance.
منابع مشابه
Shallow Parsing with Conditional Random Fields
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random fiel...
متن کاملComplete Syntactic Analysis Bases on Multi-level Chunking
This paper describes a complete syntactic analysis system based on multi-level chunking. On the basis of the correct sequences of Chinese words provided by CLP2010, the system firstly has a Part-ofspeech (POS) tagging with Conditional Random Fields (CRFs), and then does the base chunking and complex chunking with Maximum Entropy (ME), and finally generates a complete syntactic analysis tree. Th...
متن کاملPart Of Speech Tagging and Chunking with HMM and CRF
In this paper we propose an approach to Part of Speech (PoS) tagging using a combination of Hidden Markov Model and error driven learning. For the NLPAI joint task, we also implement a chunker using Conditional Random Fields (CRFs). The results for the PoS tagging and chunking task are separately reported along with the results of the joint task.
متن کاملChunking Using Conditional Random Fields in Korean Texts
We present a method of chunking in Korean texts using conditional random fields (CRFs), a recently introduced probabilistic model for labeling and segmenting sequence of data. In agglutinative languages such as Korean and Japanese, a rule-based chunking method is predominantly used for its simplicity and efficiency. A hybrid of a rule-based and machine learning method was also proposed to handl...
متن کاملFast Full Parsing by Linear-Chain Conditional Random Fields
This paper presents a chunking-based discriminative approach to full parsing. We convert the task of full parsing into a series of chunking tasks and apply a conditional random field (CRF) model to each level of chunking. The probability of an entire parse tree is computed as the product of the probabilities of individual chunking results. The parsing is performed in a bottom-up manner and the ...
متن کامل