Min Zhang, Wenliang Chen, Xiangyu Duan, Rong Zhang
{"title":"Improving Graph-Based Dependency Parsing Models With Dependency Language Models","authors":"Min Zhang, Wenliang Chen, Xiangyu Duan, Rong Zhang","doi":"10.1109/TASL.2013.2273715","DOIUrl":null,"url":null,"abstract":"For graph-based dependency parsing, how to enrich high-order features without increasing decoding complexity is a very challenging problem. To solve this problem, this paper presents an approach to representing high-order features for graph-based dependency parsing models using a dependency language model and beam search. Firstly, we use a baseline parser to parse a large-amount of unannotated data. Then we build the dependency language model (DLM) on the auto-parsed data. A set of new features is represented based on the DLM. Finally, we integrate the DLM-based features into the parsing model during decoding by beam search. We also utilize the features in bilingual text (bitext) parsing models. The main advantages of our approach are: 1) we utilize rich high-order features defined over a view of large scope and additional large raw corpus; 2) our approach does not increase the decoding complexity. We evaluate the proposed approach on the monotext and bitext parsing tasks. In the monotext parsing task, we conduct the experiments on Chinese and English data. The experimental results show that our new parser achieves the best accuracy on the Chinese data and comparable accuracy with the best known systems on the English data. In the bitext parsing task, we conduct the experiments on a Chinese-English bilingual data and our score is the best reported so far.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2273715","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2273715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
For graph-based dependency parsing, how to enrich high-order features without increasing decoding complexity is a very challenging problem. To solve this problem, this paper presents an approach to representing high-order features for graph-based dependency parsing models using a dependency language model and beam search. Firstly, we use a baseline parser to parse a large-amount of unannotated data. Then we build the dependency language model (DLM) on the auto-parsed data. A set of new features is represented based on the DLM. Finally, we integrate the DLM-based features into the parsing model during decoding by beam search. We also utilize the features in bilingual text (bitext) parsing models. The main advantages of our approach are: 1) we utilize rich high-order features defined over a view of large scope and additional large raw corpus; 2) our approach does not increase the decoding complexity. We evaluate the proposed approach on the monotext and bitext parsing tasks. In the monotext parsing task, we conduct the experiments on Chinese and English data. The experimental results show that our new parser achieves the best accuracy on the Chinese data and comparable accuracy with the best known systems on the English data. In the bitext parsing task, we conduct the experiments on a Chinese-English bilingual data and our score is the best reported so far.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.