Query-Free News Search
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1 Query-Free News Search by Monika Henzinger, Bay-Wei Chang, Sergey Brin - Google Inc. Brian Milch - UC Berkeley presented by Martin Klein, Santosh Vuppala {mklein, svuppala}@cs.odu.edu ODU, Norfolk, 03/21/2007
2 Introduction about news search supporting TV broadcast automatically selecting web pages/news articles relevant to the ongoing stream of TV broadcast news presented to user while watching TV
3 Outline Query Generation (QG) Algorithms Postprocessing Evaluation Conclusion Discussion
4 Examples
5 Examples
6 How is it done? extract queries from closed caption query generation issue queries to news search engine on the web determine what news articles to provide to the user post processing of top results
7 Query Generation relevant articles shown at regular rate during news broadcast ergo, QG needs to produce a query periodically (every S seconds) S = 7 15 (15sec eq ~3 sentences, ~50 words) QG is given the text segment T since the last query generation 7 different QG algorithms to create 2 or 3 word queries
8 TF-IDF Review tf - how often this term appears in text segment T idf - in how many text segments does term appear idf = log(n/f+1) with: N - total number of text segments f - number of text segments in which term appears
9 The Baseline Algorithm A1-BASE simple, calculates the product of tf and idf larger weights for more frequent terms larger weights for more unusual terms returns the two terms with largest weight as the query
10 The A2-IDF2 Algorithm same as baseline but: calculates weight of terms as tf * idf * idf gives even more importance to rare words
11 The Simple Stemming Algorithm A3-STEM assigns a weight to each stem stem of a word is approximated by taking first 5 letters e.g. sports, sportsman, sportive sport for each stem, store all terms (plus their weight) that generated stem c * tf * idf * idf (c = 1 for nouns or else 0.5) top weighted terms for top 2 stems form query
12 The stemming algorithm with compounds A4-COMP A3-STEM + two-word compounds list of allowed compounds (New York, San Francisco) e.g. stem for the compound veterans administration is veter-admin query formed as in A3-STEM can consist of 2, 3 or 4 words now
13 The History Algorithm A5-HIST A4-COMP + history feature uses terms from previous text segments to aid in generating query for the current one data structure called stem vector represents previously seen text (stems, terms, weight) SV combines history information with information (produced by A4-COMP) for current text segment finds the top weighted stems
14 The History Algorithm A5-HIST reset to empty stem vector new SV created for each new T computes similarity score between current T and text in old SV threshold to determine if similar, somewhat similar and dissimilar if similar: SVs are merged if dissimilar: reset old SV and replace with new SV
15 The Query Shortening Algorithm A6-3W identical to A5-HIST issues a multiple term query shortens the query until results are returned from news search engine starts with three terms
16 Algorithm A7-IDF identical to A5-HIST tf * idf replaced by tf * idf * idf
17 Postprocessing issue search queries to news search engine & retrieve top 15 results result contains one news article ~40% duplicates in returned articles near-duplicate backoff strategy detected by comparing titles and summaries if considered as duplicate, dismissed, next in line chosen (if empty, chose 1st)
18 Postprocessing - Boosting use additional high-weighted terms to select most relevant articles (from search results) QG returns: boost terms - top 5 terms (like Q-terms) boost value - their IDF values computes weight for each result re-ranking
19 Postprocessing Similarity Re-ranking similarity between text segment and returned results is computed tf-idf weighted term vectors for text segment and text of article (first 500 chars) compute normalized cosine similarity score can you see the downside?
20 Postprocessing - Filtering discard articles very dissimilar to the caption - F1 if query too vague, top 2 results often dissimilar if found, both articles discarded, unless very similar to caption (cosine similarity score (css)) - F2 F1: if css(t) of page A < threshold b, discard F2: if css of two pages A, B < threshold p, discard BUT if css(t) of A or B > threshold g, retain page (F3)
21 EVALUATION by humans 0 - if the article is not on the topic 1 - if the article is about the topic in general, but not the exact story 2 - if the article is about the exact news story that is being discussed relevant (R) if score of 1 very relevant (R+) if score = 2
22 EVALUATION use Precision and relative Recall to compare algorithms measure topic coverage number of topics with 1 relevant articles
23 Data Sets HN (Headline News): Three 30 minute sessions of CNN headline news, each taken from a different day topics comprise ~ 70min CNN: One hour of Wolf Blitzer Reports on CNN from one day and 30 minutes from another day topics comprise ~ 64min
24 Evaluation of the QG Algorithms
25 Evaluation of the QG Algorithms idf * idf seems to work slightly better than just idf experiments on stemming are inconclusive adding compounds does not improve precision adding history feature is recommended 3-word queries only good without postprocessing
26 Evaluation of Postprocessing filtering gains 20-30% in precision loses 6% in recall filtering + sim. rerank same precision but better recall
27 Query Overlap and URL Overlap reason for similarity in performance of the different query selection algorithms? issue similar queries? return similar URLs?
28 Query Overlap % of queries that have identical terms identical only for similar algorithms
29 URL Overlap % of URLs returned by 2 algorithms high only for similar algorithms
30 Topic Coverage ~70% of the topics have 1 relevant article
31 Filtering Effectiveness % of articles filtered out by what filter rule
32 Filtering Effectiveness how often does a filtering rule make wrong decision?
33 Conclusion 7 algorithms and 3 postprocessing techniques to find news articles on the web top algorithms find relevant article every 16-20sec precision 84%-91% relevant article found in 70% of topics filtering by similarity to caption and with each other improves precision not restricted to news...
34 Background - Authors Brian Milch PhD in CS from UC Berkeley now Post-Doc at MIT, Cambridge, MA Monika Henzinger, Sergey Brin Bay-Wei Chang ( Who Links to Whom:... paper) picture from:
35 Background - Paper published at WWW2003 (Budapest, Hungary) journal article as well (2005) last paper Brin has published (DBLP Bibliography Server)
36 Discussion We have seen that these algorithms work fairly well, they return ~70% of topics with relevant or super relevant articles. Can you think of cases where it would not work? Do you think the algorithms would still achieve ~70% if there were more than 3 options to chose from to evaluate the relevance of the articles? What if they had used a web-scale general topic search engine? Do we know now how to do Query-Free Search? Can we go home?
37 Of Course Not... dimension of the research area We do not have closed captions everywhere! What are movies like Rain Man and Fight Club about? this is how much the paper covers
38
39 References
Query-Free News Search
Query-Free News earch Monika Henzinger Google Inc. 2400 Bayshore Parkway Mountain View, CA 94043 UA monika@google.com Brian Milch UC Berkeley Computer cience Division Berkeley, CA 94720-1776 UA milch@cs.berkeley.edu
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