Croft, W.B.; Metzler, D.; Strohman, T.: Search engines : information retrieval in practice (2010)
0.14
0.13513544 = product of:
0.20270315 = sum of:
0.10651682 = weight(_text_:search in 2605) [ClassicSimilarity], result of:
0.10651682 = score(doc=2605,freq=14.0), product of:
0.1747324 = queryWeight, product of:
3.475677 = idf(docFreq=3718, maxDocs=44218)
0.05027291 = queryNorm
0.6095997 = fieldWeight in 2605, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
3.475677 = idf(docFreq=3718, maxDocs=44218)
0.046875 = fieldNorm(doc=2605)
0.09618632 = product of:
0.19237264 = sum of:
0.19237264 = weight(_text_:engines in 2605) [ClassicSimilarity], result of:
0.19237264 = score(doc=2605,freq=10.0), product of:
0.25542772 = queryWeight, product of:
5.080822 = idf(docFreq=746, maxDocs=44218)
0.05027291 = queryNorm
0.75313926 = fieldWeight in 2605, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
5.080822 = idf(docFreq=746, maxDocs=44218)
0.046875 = fieldNorm(doc=2605)
0.5 = coord(1/2)
0.6666667 = coord(2/3)
- Abstract
- For introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice, is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book's numerous programming exercises make extensive use of Galago, a Java-based open source search engine. SUPPLEMENTS / Extensive lecture slides (in PDF and PPT format) / Solutions to selected end of chapter problems (Instructors only) / Test collections for exercises / Galago search engine
- LCSH
- Search engines / Programming
- Subject
- Search engines / Programming
Kim, Y.; Seo, J.; Croft, W.B.; Smith, D.A.: Automatic suggestion of phrasal-concept queries for literature search (2014)
0.07
0.06863054 = product of:
0.102945805 = sum of:
0.06709928 = weight(_text_:search in 2692) [ClassicSimilarity], result of:
0.06709928 = score(doc=2692,freq=8.0), product of:
0.1747324 = queryWeight, product of:
3.475677 = idf(docFreq=3718, maxDocs=44218)
0.05027291 = queryNorm
0.3840117 = fieldWeight in 2692, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.475677 = idf(docFreq=3718, maxDocs=44218)
0.0390625 = fieldNorm(doc=2692)
0.03584652 = product of:
0.07169304 = sum of:
0.07169304 = weight(_text_:engines in 2692) [ClassicSimilarity], result of:
0.07169304 = score(doc=2692,freq=2.0), product of:
0.25542772 = queryWeight, product of:
5.080822 = idf(docFreq=746, maxDocs=44218)
0.05027291 = queryNorm
0.2806784 = fieldWeight in 2692, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.080822 = idf(docFreq=746, maxDocs=44218)
0.0390625 = fieldNorm(doc=2692)
0.5 = coord(1/2)
0.6666667 = coord(2/3)
- Abstract
- Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.
