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Implicit Queries for Email Vitor R. Carvalho (Joint work with Joshua Goodman, at Microsoft Research)

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Presentation on theme: "Implicit Queries for Email Vitor R. Carvalho (Joint work with Joshua Goodman, at Microsoft Research)"— Presentation transcript:

1 Implicit Queries for Vitor R. Carvalho (Joint work with Joshua Goodman, at Microsoft Research)

2 2 Search + is the number 1 activity on the internet Fast, easy and cheap Search is number 2 Highly lucrative (billion market – targeted ads) Why not put them together? Make users happy Make more money

3 3

4 4 Implicit Queries for Find good search keywords in messages 1 Click (or less) for users to do search Lots of possible User Interfaces Add hyperlinks to words in message List keywords in a sidebar Perform search automatically; show results (Gmail) Closely related to finding keywords for advertising

5 5 Main Contributions 1)Extract Keyphrases Similar to Information Extraction Several features 2) Rank/Display Maxent probability estimates 3) Select/Filter Restrict to MSN Query Logs (7.5 million entries)

6 6 Dataset  20 Hotmail volunteers (not MS employees)  Spam, “subs” and “wanted” folders  6 human annotators labeled 1143 msgs according to the following instructions: These are mail messages from real Hotmail users. Imagine that you were the recipient of each message. If your program were to automatically perform a query to a search engine like MSN Search or Google for you, what words or phrases would you want the engine to search for? In some messages, there may be no words worth searching for. In others, there may be several. When possible, the words or phrases should actually occur in the messages you annotate.

7 7 TF-IDF baseline  Extract all possible keyphrases from (up to 5 tokens)  Rank keyphrases by TF-IDF scores  TF = term frequency: number of times each keyphrase occurs in the message  IDF = 1/DF = number of documents the keyphrase occurs in corpus  Top1 – percentage of “ranked-1 st keyphrases” that were labeled as relevant  Top10 – number of keyphrases in the top-10 rank that were labeled as relevant, normalized by the total number of relevant keyphrases (no message had more than 10 relevant keyphrases) Keyphrases TF-IDF Port Angeles0.450 Lake Crescent0.120 Atlanta0.090 Mt. Baker0.045 …… Top-1 (%) Top-10 (%) TF-IDF

8 8 First Improvement: Constrain Results to Query Log File  Query log file: top 7.5 million queries to MSN Search  Only return keyphrases from an if they occur in the query log file Faster – only process keyphrases in message that occur in the query log file. Creates some errors Removes some errors – such as “occur in the” Works better! Top-1 (%) Top-10 (%) TF-IDF TF-IDF with query log restriction

9 9 Adding More Features 1) Query Log Frequency Frequency and log(frequency) of keyphrase 2) Capitalization Word capitalized before/after, # capitalized initials in phrase, # capitalized letters in phrase, etc 3) Phrase Length Number of characters and number of tokens 4) TF + IDF based features TF, IDF, from Body and from Subject 5) Punctuation and Alphanumeric Punct before/after, has no alpha, has numbers only, etc 6) Specific Has FW: in subject, has RE: in subject

10 10 Maximum Entropy Learner (a.k.a. Logistic Regression)  Computes  y is 1 if keyphrase is relevant  is the feature vector (previous slide features)  Weight vector w learned using a type of Generalized Iterative Scaling alg. (SCGIS).  Rank and cutoff based on probability estimate

11 11 Rank and cutoff based on probability Keyphrases Port Angeles Lake Crescent Olympic National Park Atlanta Mt. Baker Hurricane Ridge Marymere Fall Beaches on the west coast Probability Cutoff = 10%

12 12 Performance Analysis Top-1Top-10 TF-IDF (one single feature and no query log restriction) TF-IDF (one single feature) Baseline → 2 features: TF and IDF Baseline + Query Frequency 23.13*41.82* Baseline + Phrase Length Baseline + Capitalization 21.43*44.71* Baseline + Punctuation Baseline + Specific Baseline + Alphanumeric Baseline + All Features 33.55*55.26* 10-fold cross-validation on the messages

13 13 Performance Analysis

14 14 Using Other Learning Algorithms

15 15 Opportunities for Future Work 1. Relax the Query Log restriction 2. Use real advertisement data 3. Use feedback from users (user can be annoyed, etc) 4. Use personalization (age, gender, place, etc)

16 16 Conclusions Implicit Query task → finding good search keywords Use of large query log from MSN Search Maxent to combine features and output probabilities – ranking and display cutoff Most meaningful features are associated with query frequency and capitalization Results several times better than baseline TF-IDF (top 1 and top 10 scores)

17 17 Thank you


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