کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
568490 1452020 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Exploring the use of unsupervised query modeling techniques for speech recognition and summarization
ترجمه فارسی عنوان
بررسی استفاده از تکنیک های مدل سازی جستجوهای بدون نظارت برای تشخیص گفتار و تلخیص
کلمات کلیدی
مدل سازی پرس و جو. زبان مدل سازی؛ بازیابی اطلاعات؛ تشخیص گفتار؛ خلاصه سازی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

Statistical language modeling (LM) that intends 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 major contributions are three-fold. First, we propose a principled framework which can unify the relationships among several widely-cited query modeling formulations. Second, on top of this successfully developed framework, two extensions have been proposed. On one hand, we present an extended query modeling formulation by incorporating critical query-specific information cues to guide the model estimation. On the other hand, a word-based relevance modeling has also been leveraged to overcome the obstacle of time-consuming model estimation when the framework is being utilized for practical applications. In addition, we further adopt and formalize such a framework to the speech recognition and summarization tasks. A series of experiments reveal the empirical potential of such an LM framework and the performance merits of the deduced models on these two tasks.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Speech Communication - Volume 80, June 2016, Pages 49–59
نویسندگان
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