Mining Chinese comparative sentences by semantic role labeling
Published 1 July 2008
Feng Hou, Guohui Li
Citations20
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper studies the problem of mining Chinese comparative sentences in text documents by using semantic role labeling (SRL), and new comparative sentences were labeled by using this CRFs model, and comparative relations were extracted afterward.
Abstract
This paper studies the problem of mining Chinese comparative sentences in text documents by using Semantic Role Labeling (SRL). The comparative opinion can be divided into six semantic roles: Holder, Entity 1, Comparative predicates, Entity 2, Attributes and Sentiments. These six opinion elements were recognized and labeled by using SRL. A corpus of Chinese comparative sentences was manually labeled at first. Then a Conditional Random Fields (CRFs) model was trained by learn from the corpus. Finally new comparative sentences were labeled by using this CRFs model, and comparative relations were extracted afterward.
Keywords
Computer Science
ScholarlyCommons (University of Pennsylvania)Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
12,978 Citations2001John Lafferty, Andrew McCallum +1 more
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Mining and summarizing customer reviews
7,714 Citations2004Minqing Hu, Bing Liu
This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
Computational LinguisticsAutomatic Labeling of Semantic Roles
1,647 Citations2002Daniel Gildea, Daniel Jurafsky
Determining the sentiment of opinions
1,485 Citations2004Soo-Min Kim, Eduard Hovy
A system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion and another module for determining word sentiment and another for combining sentiments within a sentence is presented.
Learning extraction patterns for subjective expressions
1,001 Citations2003Ellen Riloff, Janyce Wiebe
A bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions while maintaining high precision is presented.
Identifying comparative sentences in text documents
445 Citations2006Nitin Jindal, Bing Liu
This paper first categorizes comparative sentences into different types, and then presents a novel integrated pattern discovery and supervised learning approach to identifying comparative sentences from text documents.
Mining comparative sentences and relations
338 Citations2006Nitin Jindal, Bing Liu
This paper proposes two novel techniques based on two new types of sequential rules to perform the tasks of comparative sentence mining and results show that these techniques are very promising.
Semantic role labelling with tree conditional random fields
94 Citations2005Trevor Cohn, Philip Blunsom
This paper shows how modelling the semantic role labelling task as a tree labelling problem allows for the use of efficient CRF inference algorithms, while also increasing generalisation performance when compared to the equivalent maximum entropy classifier.
Automatic semantic role labeling for Chinese verbs
94 Citations2005Nianwen Xue, Martha Palmer
The results using hand-crafted parses are slightly higher than the results reported for the state-of-the-art semantic role labeling systems for English using the Penn English Proposition Bank data, even though the Chinese Proposition Bank is smaller in size.
North American Chapter of the Association for Computational LinguisticsShallow Semantc Parsing of Chinese.
80 Citations2004Honglin Sun, Daniel Jurafsky
It is shown that good semantic parsing results for Chinese can be achieved with a small 1100-sentence training set, and Porting the Collins parser to Chinese is described, resulting in the best performance currently reported on Chinese syntactic parsing.
Annotation Guidelines for the Chinese Proposition Bank
4 Citations2007Nianwen Xue
The purpose of the Chinese PropBank project is to add a layer of annotation to the hand-parsed sentences in the Chinese Treebank (CTB) to assign predicatespecific argument labels to the constituents in a parse tree.