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Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF (Paperback, 1st ed. 2019)
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Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF (Paperback, 1st ed. 2019)
Series: The Information Retrieval Series, 41
Expected to ship within 10 - 15 working days
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This volume celebrates the twentieth anniversary of CLEF - the
Cross-Language Evaluation Forum for the first ten years, and the
Conference and Labs of the Evaluation Forum since - and traces its
evolution over these first two decades. CLEF's main mission is to
promote research, innovation and development of information
retrieval (IR) systems by anticipating trends in information
management in order to stimulate advances in the field of IR system
experimentation and evaluation. The book is divided into six parts.
Parts I and II provide background and context, with the first part
explaining what is meant by experimental evaluation and the
underlying theory, and describing how this has been interpreted in
CLEF and in other internationally recognized evaluation
initiatives. Part II presents research architectures and
infrastructures that have been developed to manage experimental
data and to provide evaluation services in CLEF and elsewhere.
Parts III, IV and V represent the core of the book, presenting some
of the most significant evaluation activities in CLEF, ranging from
the early multilingual text processing exercises to the later, more
sophisticated experiments on multimodal collections in diverse
genres and media. In all cases, the focus is not only on describing
"what has been achieved", but above all on "what has been learnt".
The final part examines the impact CLEF has had on the research
world and discusses current and future challenges, both academic
and industrial, including the relevance of IR benchmarking in
industrial settings. Mainly intended for researchers in academia
and industry, it also offers useful insights and tips for
practitioners in industry working on the evaluation and performance
issues of IR tools, and graduate students specializing in
information retrieval.
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