A locality-preserving essence vector modeling framework for spoken document retrieval

Kuan Yu Chen, Shih Hung Liu, Berlin Chen, Hsin Min Wang

研究成果: 書貢獻/報告類型會議論文篇章

4 引文 斯高帕斯(Scopus)

摘要

Because unprecedented volumes of multimedia data associated with spoken documents have been made available to the public, spoken document retrieval (SDR) has become an important research area in the past decades. Recently, representation learning has emerged as an active research topic in many machine learning applications owing largely to its excellent performance. In the context of natural language processing, the pioneering work can date back to the word embedding methods. However, learning of paragraph (or sentence and document) representations is more reasonable and suitable for some tasks, such as information retrieval and document summarization. Nevertheless, as far as we are aware, there is relatively less work focusing on launching paragraph embedding methods into SDR. Motivated by these observations, this paper proposes a novel paragraph embedding method, named the locality-preserving essence vector (LPEV) model. LPEV is designed with consideration to two aspects. First, the model aims at not only distilling the most representative information from a paragraph but also getting rid of the general background information. Second, inspired by the local invariance perspective, which is a celebrated principle used in manifold learning techniques, LPEV also manages to preserve semantic locality in the learned low-dimensional embedding space for producing more informative and discriminative vector representations of paragraphs. On top of the proposed framework, a series of empirical SDR experiments conducted on the TDT-2 (Topic Detection and Tracking) collection demonstrate the good efficacy of our SDR methods as compared to existing strong baselines.

原文英語
主出版物標題2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5665-5669
頁數5
ISBN(電子)9781509041176
DOIs
出版狀態已發佈 - 2017 六月 16
事件2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, 美国
持續時間: 2017 三月 52017 三月 9

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

會議

會議2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
國家美国
城市New Orleans
期間2017/03/052017/03/09

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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