Leveraging effective query modeling techniques for speech recognition and summarization

Kuan Yu Chen, Shih Hung Liu, Berlin Chen, Ea Ee Jan, Hsin Min Wang, Wen Lian Hsu, Hsin Hsi Chen

研究成果: 書貢獻/報告類型會議論文篇章

8 引文 斯高帕斯(Scopus)

摘要

Statistical language modeling (LM) that purports to quantify the acceptability of a given piece of text has long been an interesting yet challenging research area. In particular, language modeling for information retrieval (IR) has enjoyed remarkable empirical success; one emerging stream of the LM approach for IR is to employ the pseudo-relevance feedback process to enhance the representation of an input query so as to improve retrieval effectiveness. This paper presents a continuation of such a general line of research and the main contribution is threefold. First, we propose a principled framework which can unify the relationships among several widely-used query modeling formulations. Second, on top of the successfully developed framework, we propose an extended query modeling formulation by incorporating critical query- specific information cues to guide the model estimation. Third, we further adopt and formalize such a framework to the speech recognition and summarization tasks. A series of empirical experiments reveal the feasibility of such an LM framework and the performance merits of the deduced models on these two tasks.

原文英語
主出版物標題EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
發行者Association for Computational Linguistics (ACL)
頁面1474-1480
頁數7
ISBN(電子)9781937284961
DOIs
出版狀態已發佈 - 2014 一月 1
事件2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, 卡塔尔
持續時間: 2014 十月 252014 十月 29

出版系列

名字EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

其他

其他2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014
國家/地區卡塔尔
城市Doha
期間2014/10/252014/10/29

ASJC Scopus subject areas

  • 計算機理論與數學
  • 電腦視覺和模式識別
  • 資訊系統

指紋

深入研究「Leveraging effective query modeling techniques for speech recognition and summarization」主題。共同形成了獨特的指紋。

引用此