1. Machine Learning for Personal Information Management William W. Cohen Machine Learning Department and Language Technologies Institute School of Computer Science Carnegie Mellon University Vitor Carvalho, Einat Minkov, Tom Mitchell, Andrew Ng (Stanford) and and Ramnath Balasubramanyan

2. ML for email Starting point: Ishmail, an emacs RMAIL extension written by Charles Isbell in summer ’95 (largely for Ron Brachman) Could manually write mailbox definitions and filtering rules in Lisp [Cohen, AAAI Spring Symposium on ML and IR 1996]

3. Foldering tasks Rule-learning method [Cohen, ICML95][Rocchio, 71]

4. Machine Learning in Email Why study learning for email ? 1.Email has more visible impact than anything else you do with computers. 2.Email is hard to manage: People get overwhelmed. People lose important information in email archives. People make horrible mistakes.

5. Machine Learning in Email Why study learning for email ? For which tasks can learning help ? –Foldering –Spam filtering –Search: beyond keyword search –Recognizing errors –Help for tracking tasks search don’t sort! important and well-studied “Oops, did I just hit reply-to-all?” “Dropping the ball”

6. proposal CMU CALO graph William 6/18/07 6/17/07 Sent To Term In Subject einat@cs.cmu.edu Learning to Search Email [SIGIR 2006, CEAS 2006, WebKDD/SNA 2007]

7. Email as a Graph  A closed set of 5 node types  A closed set of 18 edge types (inc. inverse edges)

8. Q: “what are Jason’s email aliases?” “Jason” Msg 5 Msg 18 jernst@ andrew.cmu.edu Sent from Email Sent to Email Jason Ernst Sent-to EmailAddressOf jernst@ cs.cmu.edu Similar to Msg 2 einat@cs.cmu.edu Sent To einat Has term inv. Basic idea: learning to search email is learning to query a graph for information

9. How do you pose queries to a graph? An extended similarity measure via graph walks:

10. Propagate “similarity” from start nodes through edges in the graph – accumulating evidence of similarity over multiple connecting paths. How do you pose queries to a graph?

11. An extended similarity measure via graph walks: Fixed probability of halting the walk at every step – i.e., shorter connecting paths have greater importance (exponential decay) Propagate “similarity” from start nodes through edges in the graph – accumulating evidence of similarity over multiple connecting paths. How do you pose queries to a graph?

12. An extended similarity measure via graph walks: Fixed probability of halting the walk at every step – i.e., shorter connecting paths have greater importance (exponential decay) In practice we can approximate with a short finite graph walk, implemented with sparse matrix multiplication Propagate “similarity” from start nodes through edges in the graph – accumulating evidence of similarity over multiple connecting paths. How do you pose queries to a graph?

13. An extended similarity measure via graph walks: Fixed probability of halting the walk at every step – i.e., shorter connecting paths have greater importance (exponential decay) In practice we can approximate with a short finite graph walk, implemented with sparse matrix multiplication The result is a list of nodes, sorted by “similarity” to an input node distribution (final node probabilities ). Propagate “similarity” from start nodes through edges in the graph – accumulating evidence of similarity over multiple connecting paths. How do you pose queries to a graph?

14. Email, contacts etc: a graph Graph nodes are typed, edges are directed and typed Multiple edges may connect two given nodes. Every edge type is assigned a fixed weight—which determines probability of being followed in a walk: e.g., uniform A query language: Q: {, } Returns a list of nodes (of type ) ranked by the graph walk probs. = query “terms” Random walk with restart, graph kernels, heat diffusion kernels, diffusion processes, Laplacian regularization, graph databases (BANKS, DbExplorer, …), graph mincut, associative Markov networks, …

15. Tasks that are like similarity queries Person name disambiguation Threading Alias finding [ term “andy” file msgId ] “person” [ file msgId ] “file”  What are the adjacent messages in this thread?  A proxy for finding “more messages like this one” What are the email-addresses of Jason ?... [ term Jason ] “email-address” Meeting attendees finder Which email-addresses (persons) should I notify about this meeting? [ meeting mtgId ] “email-address”

16. Learning to search better Query a  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50 Query b Query q  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50 … GRAPH WALK + Rel. answers a+ Rel. answers b+ Rel. answers q Task T (query class) Standard set of features used for x on each problem: Edge n-grams in all paths from V q to x Number of reachable source nodes Features of top- ranking paths (e.g. edge bigrams)

17. Graph walk Feature generation Learn re-ranker Re-ranking function Learning Node re-ordering: train task

18. Learning Approach train task Graph walk Feature generation Learn re-ranker Re-ranking function Graph walk Feature generation Score by re-ranking function Node re-ordering: Boosting test task [Collins & Koo, CL 2005; Collins, ACL 2002] Voted Perceptron; RankSVM; PerceptronCommittees; … [Joacchim KDD 2002, Elsas et al WSDM 2008]

19. Tasks that are like similarity queries Person name disambiguation Threading Alias finding [ term “andy” file msgId ] “person” [ file msgId ] “file”  What are the adjacent messages in this thread?  A proxy for finding “more messages like this one” What are the email-addresses of Jason ?... [ term Jason ] “email-address” Meeting attendees finder Which email-addresses (persons) should I notify about this meeting? [ meeting mtgId ] “email-address”

20. Corpora Nicknames: Dave for David, Kai for Keiko, Jenny for Qing Common names are ambiguous Person names Corpora and datasets PERSON NAME DISAMBIGUATION

21. CSpace Email: collected at CMU 15,000+ emails from semester-line management course students formed groups that acted as “companies” and worked together dozens of groups with some known social connections (e.g., “president”)

22. Results Mgmt. game PERSON NAME DISAMBIGUATION

23. Results Mgmt. game PERSON NAME DISAMBIGUATION

24. Results Mgmt. game PERSON NAME DISAMBIGUATION

25. Results Mgmt. game PERSON NAME DISAMBIGUATION

26. Enron: Sager-E Enron: Shapiro-R PERSON NAME DISAMBIGUATION Mgmt. Game Results On All Three Problems

27. Tasks Person name disambiguation Threading Alias finding [ term “andy” file msgId ] “person” [ file msgId ] “file”  What are the adjacent messages in this thread?  A proxy for finding “more messages like this one” What are the email-addresses of Jason ?... [ term Jason ] “email-address” Meeting attendees finder Which email-addresses (persons) should I notify about this meeting? [ meeting mtgId ] “email-address”

28. Header & Body Subject Reply lines Header & Body Subject - Header & Body - Header & Body Subject Reply lines Header & Body Subject - Header & Body - Mgmt. Game Enron: Farmer MAP 58.4 73.8 50.2 71.5 36.2 60.3 65.7 79.8 36.1 65.1 Threading: Results

29. Graph walk Weight update Theta* Learning approaches Edge weight tuning:

30. Graph walk Weight update Graph walk Learning approaches Edge weight tuning: Theta* task Graph walk Feature generation Learn re-ranker Re-ranking function Graph walk Feature generation Score by re-ranking function Node re-ordering: Boosting; Voted Perceptron Question: which is better? [Diligenti et al, IJCAI 2005; Toutanova & Ng, ICML 2005; … ]

31. Results (MAP) Name disambiguation Threading Alias finding * * * * * * * * * * * + + + ++ * Reranking and edge- weight tuning are complementary. Best result is usually to tune weights, and then rerank Reranking overfits on small datasets (meetings)

32. Machine Learning in Email Why study learning for email ? For which tasks can learning help ? –Foldering –Spam filtering –Search beyond keyword search –Recognizing errors –Help for tracking tasks “Oops, did I just hit reply-to-all?” “Dropping the ball”

34. http://www.sophos.com/

35. …

36. Idea –Goal: to detect emails accidentally sent to the wrong person –Generate artificial leaks: Email leaks may be simulated by various criteria: a typo, similar last names, identical first names, aggressive auto-completion of addresses, etc. –Method: Look for outliers. Email Leak: email accidentally sent to wrong person Email Leak Preventing errors in Email [SDM 2007]

37. Preventing Email Leaks Method –Create simulated/artificial email recipients –Build model for (msg.recipients): train classifier on real data to detect synthetically created outliers (added to the true recipient list). Features: textual(subject, body), network features (frequencies, co-occurrences, etc). –Rank potential outliers - Detect outlier and warn user based on confidence. Rec 6 Rec 2 … Rec K Rec 5 Most likely outlier Least likely outlier P(rec t ) P(rec t ) =Probability recipient t is an outlier given “message text and other recipients in the message”.

38. Enron Data Preprocessing 1 Realistic scenario –For each user, 10% (most recent) sent messages will be used as test Construct Address Books for all users –List of all recipients in the sent messages.

39. Simulating Leaks Several options: –Frequent typos, same/similar last names, identical/similar first names, aggressive auto-completion of addresses, etc. In this paper, we adopted the 3g-address criteria: –On each trial, one of the msg recipients is randomly chosen and an outlier is generated according to: Else: Randomly select an address book entry 1 2 3 Marina.wang @enron.com 1-α α Random non- address-book address

40. Enron Data Preprocessing 2 ISI version of Enron –Remove repeated messages and inconsistencies Disambiguate Main Enron addresses –List provided by Corrada-Emmanuel from UMass Bag-of-words –Messages were represented as the union of BOW of body and BOW of subject Some stop words removed Self-addressed messages were removed

41. Experiments: using Textual Features only Three Baseline Methods –Random Rank recipient addresses randomly –Rocchio/TfIdf Centroid [Rocchio 71] Create a “TfIdf centroid” for each user in Address Book. A user1-centroid is the sum of all training messages (in TfIdf vector format) that were addressed to user user1. For testing, rank according to cosine similarity between test message and each centroid. –Knn-30 [Yang and Chute, SIGIR 94] Given a test msg, get 30 most similar msgs in training set. Rank according to “sum of similarities” of a given user on the 30-msg set.

42. Experiments: using Textual Features only Email Leak Prediction Results: Prec@1 in 10 trials. On each trial, a different set of outliers is generated

43. Network Features How frequent a recipient was addressed How these recipients co- occurred in the training set

44. Using Network Features 1.Frequency features –Number of received messages (from this user) –Number of sent messages (to this user) –Number of sent+received messages 2.Co-Occurrence Features –Number of times a user co-occurred with all other recipients. 3.Max3g features –For each recipient R, find Rm (=address with max score from 3g-address list of R), then use score(R)-score(Rm) as feature. Combine with text-only scores using voted- perceptron reranking, trained on simulated leaks

45. Precision at rank 1 α = 0

46. Finding Real Leaks in Enron How can we find it? –Grep for “mistake”, “sorry” or “accident”. –Note: must be from one of the Enron users Found 2 good cases: 1.Message germanyc/sent/930, message has 20 recipients, leak is alex.perkins@ 2.kitchen-l/sent items/497, it has 44 recipients, leak is rita.wynne@

47. Results on real leaks Kitchen-l has 4 unseen addresses out of the 44 recipients, Germany-c has only one.

48. The other kind of recipient error How accurately can you fill in missing recipients, using the message text as evidence? Mean average precision over 36 users, after using thread information [ECIR 2008]

49. Leak warnings: hit x to remove recipient Pause or cancel send of message Timer: msg is sent after 10sec by default Suggestions: hit + to add Current prototype (Thunderbird plug-in) Classifier/rankers written in JavaScript

50. Machine Learning in Email Why study learning for email ? For which tasks can learning help ? –Foldering –Spam filtering –Search beyond keyword search –Recognizing errors –Help for tracking tasks “Dropping the ball”

51. Dropping the Ball

52. Request Time/date Check off Task: “get new screen shots for kickoff talk” -? [Minkov et al, IJCAI 2005; ACL 2005]

53. Speech Acts for Email [EMNLP 2004, SIGIR 2005, ACL Acts WS 2006]

54. Classifying Speech Acts [Carvalho & Cohen, SIGIR 2005]: Relational model including adjacent messages in thread; pseudo-likelihood/RDN model with annealing phase. [Carvalho & Cohen, ACL workshop 2006: IE preprocessing, n-grams, feature extraction, YFLA] ** Ciranda package [Dabbish et al, CHI 05; Drezde et al, IUI 06; Khoussainov & Kushmeric, CEAS 2006; Goldstein et al, CEAS 2006; Goldstein & Sabin, HICCS 06]

56. Request Time/date Add Task: follow up on: “request for screen shots” by ___ days before -? 2 “next Wed” (12/5/07) “end of the week” (11/30/07) “Sunday” (12/2/07) - other -

57. Commitment Time/date Add Task: “METAL – fairly urgent feedback sought” by “tomorrow noon” (11/29/07) - other - Warning! You are making a commitment! Hit cancel to abort!

58. Conclusions/Summary Email is visible and important There are lots of interesting problems associated with email processing –Learning to query heterogeneous data graphs –Modeling patterns of interactions User  User textual communication User  User commun. frequency, recency, … –… to predict likely recipients/nonrecipients, correct possible errors, and/or aid user in tracking requests and commitments  perfect ML application

59. Conclusions/Summary Email is visible and important There are lots of interesting problems associated with email processing –Learning to query heterogeneous data graphs –Modeling patterns of interactions User  User textual communication User  User commun. frequency, recency, … –… to predict likely recipients/nonrecipients, correct possible errors, and/or aid user in tracking requests and commitments

60. Bibliography: Our Group Einat Minkov and William Cohen (2007): Learning to Rank Typed Graph Walks: Local and Global Approaches in WebKDD-2007. Vitor Carvalho, Wen Wu and William Cohen (2007): Discovering Leadership Roles in Email Workgroups in CEAS-2007. Vitor Carvalho and William Cohen (2007): Ranking Users for Intelligent Message Addressing, to appear in ECIR-2008. Vitor Carvalho and William W. Cohen (2007): Preventing Information Leaks in Email in SDM-2007. Einat Minkov and William W. Cohen (2006): An Email and Meeting Assistant using Graph Walks in CEAS-2006. Einat Minkov, Andrew Ng and William W. Cohen (2006): Contextual Search and Name Disambiguation in Email using Graphs in SIGIR-2006. Vitor Carvalho and William W. Cohen (2006): Improving Email Speech Act Analysis via N-gram Selection in HLT/NAACL ACTS Workshop 2006. William W. Cohen, Einat Minkov & Anthony Tomasic (2005): Learning to Understand Web Site Update Requests in IJCAI-2005. Einat Minkov, Richard C. Wang, and William W. Cohen (2005): Extracting Personal Names from Email: Applying Named Entity Recognition to Informal Text in EMNLP/HLT- 2005. Vitor Carvalho & William W. Cohen (2005): On the Collective Classification of Email Speech Acts in SIGIR 2005. William W. Cohen, Vitor R. Carvalho & Tom Mitchell (2004): Learning to Classify Email into "Speech Acts" in EMNLP 2004. Vitor R. Carvalho & William W. Cohen (2004): Learning to Extract Signature and Reply Lines from Email in CEAS 2004.

61. Bibliography: Other Cited Papers M. Collins. Ranking algorithms for named-entity extraction: Boosting and the voted perceptron. In ACL, 2002. [M. Collins and T. Koo. Discriminative reranking for natural language parsing. Computational Linguistics, 31(1):25–69, 2005. M. Diligenti, M. Gori, and M. Maggini. Learning web page scores by error back- propagation. In IJCAI, 2005. T. Joachims, Optimizing Search Engines Using Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002. Jonathan Elsas, Vitor R. Carvalho and Jaime Carbonell. Fast Learning of Document Ranking Functions with the Committee Perceptron. WSDM-2008 (ACM International Conference on Web Search and Data Mining). Y. Yang and C. G. Chute, “An example-based mapping method for text classification and retrieval”, ACM Trans Info Systems, 12(3), 1994. L. A. Dabbish, R. E. Kraut, S. Fussell, and S. Kiesler, “Understanding email use: predicting action on a message,” in CHI ’05: Proceedings of the SIGCHI conference on Human factors in computing systems, 2005, pp. 691–700.

62. Bibliography: Other Cited Papers M. Dredze, T. Lau, and N. Kushmerick, “Automatically classifying emails into activities,” in IUI ’06: Proceedings of the 11th international conference on Intelligent user interfaces, 2006, pp. 70–77. D. Feng, E. Shaw, J. Kim, and E. Hovy, “Learning to detect conversation focus of threaded discussions,” in Proceedings of the HLT/NAACL 2006 (Human Language Technology Conference - North American chapter of the Association for Computational Linguistics), New York City, NY, 2006. J. Goldstein, A. Kwasinksi, P. Kingsbury, R. E. Sabin, and A. McDowell, “Annotating subsets of the enron email corpus,” in Conference on Email and Anti-Spam (CEAS’2006), 2006. J. Goldstein and R. E. Sabin, “Using speech acts to categorize email and identify email genres,” Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), vol. 3, p. 50b, 2006. [11] R. Khoussainov and N. Kushmerick, “Email task management: An iterative relational learning approach,” in Conference on Email and Anti- Spam (CEAS’2005), 2005. David Allen, “Getting things done: the art of stress-free productivity”, Penguin Books, 2001.