کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4966377 1448891 2018 13 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
A Prospect-Guided global query expansion strategy using word embeddings
ترجمه فارسی عنوان
یک استراتژی گسترش جهانی پرس و جو با استفاده از واژه های تعبیه شده
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کلمات کلیدی
گسترش جهانی پرس و جو؛ دکمه های کلمه .بازیابی اطلاعات؛ روش های تلفیقی. شرایط نامزدی؛
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


- Global query semantics modeled from the standpoint of prospect vocabulary terms.
- Selective semantic exploration strategy adds new terms related to more relevant topics.
- Disambiguation issues addressed without exogenous resources.
- Significant results improving both recall and precision metrics without relevance feedback.

The effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query semantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary semantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves significant results that outperform the baselines, improving both recall and precision metrics without relevance feedback.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Processing & Management - Volume 54, Issue 1, January 2018, Pages 1-13
نویسندگان
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