1. Query Segmentation Using Conditional Random Fields Xiaohui and Huxia Shi York University KEYS’09 (SIGMOD Workshop) 2009. 07. 16. Presented by Jaehui Park, IDS Lab., Seoul National University

2. Copyright  2008 by CEBT INTRODUCTION  effective search of text information in relational databases keyword search – one of the challenges assembling keyword-matching tuples from different tables into one view  exponential search space (w.r.t. the number of keywords) 2 idnamecolormanufac.size id1T41blackcid112 id2T60Silvercid17 id3MiniBluecid34 id4vaiograycid27 id5vaiopredcid22 id6xnoteblackcid47 idnameheadquarter cid1IBMChina cid2 SONYJapan cid3 DELLUSA cid4 samsungKorea ProductCompany id1T41Blackcid112 cid1IBMChina T41 IBM query …

3. Copyright  2008 by CEBT INTRODUCTION  ex) “Green Mile Tom Hanks” segment: > – segment-matching (not keyword-matching) reducing the search space  Conditional Random Fields (CRF) probabilistic model to segment and label sequence data – normalized model for multiple feature function combination outperform Hidden Markov Model and Maximum-Entropy Markov Model in real world labeling tasks alleviating independence assumption avoid label bias assumption model a conditional probability distribution over a label sequence given keyword sequence – for given “Green Mile Tom Hanks” 3

4. Copyright  2008 by CEBT PROBLEM DEFINITION  query an ordered keyword sequence ( x = )  segment a subsequence of keywords in the query ( S = ) is valid – if this subsequence appears at least once in the database D  segmentation a sequence of non-overlapping segments that completely cover all keywords in the query ( Š = ) is valid – iff all S ∈ Š are valid – ex) query: Star Wars Clone valid segmentations  ( )  To find the optimal segmentation 4

5. Copyright  2008 by CEBT NON-STATISTICAL ALGORITHMS  greedy search starting with the first keyword in a given query keep including the next keyword into the current segment until adding the new keyword would make the segment no longer valid : not valid … 5

6. Copyright  2008 by CEBT NON-STATISTICAL ALGORITHMS  Keyword Query Cleaning [VLDB 2008] dynamic programming expanding each keyword to a set of similar tokens scoring function – TF-IDF (IR sense) – favors longer segments – penalizes spelling corrections 6

7. Copyright  2008 by CEBT QUERY SEGMENTATION USING CRF  Computing the label for each keyword in a given query grouping adjacent keywords with the same label into the same segment – conditional probability for y: label sequence x: keyword sequence – best label sequence y’ = training set – Database D = {x k, y k } k=1~N obtained from query logs 7

8. Copyright  2008 by CEBT QUERY SEGMENTATION USING CRF  the query is segmented based on those label the invalid segments is further broken down into valid segments – “Green Mile Tom Hanks” > – “Johny Depp Orlando Bloom” MaxScore, MaxTerm algorithm computing optimal segmentation S’ = – the optimal segmentation for each invalid segment is computed through the tree search procedure MaxTerm: valid segment of maximum length  E> from finest segment to MaxTerm 8 …JDOB… … JO B D

9. Copyright  2008 by CEBT QUERY SEGMENTATION USING CRF  enhanced CRF model column-position pairing – exact position of a keyword in a segment – ex) “Green Mile Tom Hanks”  start position  segmentation boundary  other position  adapting user preferences 9

10. Copyright  2008 by CEBT EXPERIMENTS  Dataset IMDB (9839026 tuples), FoodMark (428049 tuples) the training set and test queries are generated by random sampling – 10-fold cross validation segmentation accuracy – A x = 1 – (|Sx – S’x| + |S’x – Sx|) / |Sx| Sx: true segment set S’x: predicted segment set  accuracy the CRF model is not sensitive to query length 10

11. Copyright  2008 by CEBT EXPERIMENTS  ambiguous connection deciding which segment a keyword should belong when that keyword can form valid segments with both the preceding and the following keywords –, k,, …> – ambiguity level the number of ambiguous connections 11

12. Copyright  2008 by CEBT EXPERIMENTS  efficient query segmentation two or three orders of magnitude improvement over keyword query cleaning less than 0.02 seconds to segment one medium query (except keyword query cleaning) 12

13. Copyright  2008 by CEBT CONCLUSION  CRF-based models for query segmentation  Experiments have demonstrated the effectiveness of the proposed approach  Future work accommodating spelling errors online segmentation of query in a streaming fashion 13

14. Copyright  2008 by CEBT discussion  ( ) subsequence of valid segment is also valid  strong assumptions prefix as a subsequence of keywords considering only adjacent keywords in the same segment  not clear sum of all segments is always constant (MaxScore algorithm) – it cannot solve the problem of two terms in a segment hard to follow the tree merging algorithm (MaxTermSearch algorithm) 14