Query Expansion with Locally-Trained Word Embeddings (Neu-IR 2016)
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Transcript of Query Expansion with Locally-Trained Word Embeddings (Neu-IR 2016)
QueryExpansionwithLocally-TrainedWordEmbeddings
Fernando Diaz Bhaskar Mitra NickCraswell
Microsoft
July21, 2016
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L =T∑
t=1
ωxt︸︷︷︸termweight
∑y∈Vt
c
logσ(ϕ(xt) · ϕ(y))︸ ︷︷ ︸observedcontext
+∑y∈Vt
n
logσ(−ϕ(xt) · ϕ(y))︸ ︷︷ ︸negativecontext
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LanguageModelScoring
score(d, q) = KL(θq, θd)
θq maximumlikelihoodquerylanguagemodelθd documentlanguagemodel
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QueryExpansionwithWordEmbeddings
Uglobal embeddingtrainedwith p(w|C)Ulocal embeddingtrainedwith p(w|R)
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Data
docs words queriestrec12 469,949 438,338 150robust 528,155 665,128 250web 50,220,423 90,411,624 200giga 9,875,524 2,645,367 -wiki 3,225,743 4,726,862 -
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Embeddings
• global• publicembeddings(GloVe, word2vec)• word2vecontargetcorpus
• local: word2vecwithdocumentssampledby p(d)
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Results
global localwiki+giga gnews target target giga wiki
QL 50 100 200 300 300 400 400 400 400trec12 0.514 0.518 0.518 0.530 0.531 0.530 0.545 0.535 0.563* 0.523robust 0.467 0.470 0.463 0.469 0.468 0.472 0.465 0.475 0.517* 0.476web 0.216 0.227 0.229 0.230 0.232 0.218 0.216 0.234 0.236 0.258*
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