Semantic Search in E-Discovery. David Graus & Zhaochun Ren

Transcription

1 Semantic Search in E-Discovery David Graus & Zhaochun Ren

2 This talk Introduction David Graus! Understanding traffic David Graus! Topic discovery & tracking in social media Zhaochun Ren 2

3 Intro Semantic Search in E-Discovery NWO-funded project 4 year, 2 PhD students With help/input from: NFI FIOD Create-IT Applied Research FoxIT 3

4 Semantic Search in E-Discovery Information Retrieval + Information Extraction/Text Mining 4

5 Semantic Search in E-Discovery Information Retrieval Finding material of unstructured nature from large collections + Information Extraction (Text Mining) 5

6 Semantic Search in E-Discovery Information Retrieval Finding material of unstructured nature from large collections + Information Extraction (Text Mining) Discovering patterns in data 6

7 Focus Forensic evidence in user-generated content , social media, forums, etc. 7

8 Challenge Finding out who knew what, from whom, and when 8

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10 Understanding traffic David Graus

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12 Recipient recommendation Given a sender, an , all possible recipients (in an enterprise); Predict which recipient(s) are most likely to receive the 12

13 Why? Understanding communication in/structure of an enterprise Applications in: enterprise search expert finding community detection spam classification anomaly detection 13

14 How? Gmail Who do you frequently co-address egonetwork Related work Us Social Network Analysis (SNA) content SNA + content 14

15 Part 1: Social Network Analysis? 15

16 image by Calvinius - Creative Commons Attribution-Share Alike

17 SNA for predicting recipients? 1. Importance of a node in the network More important people are more likely to be the recipient of an 2. Strength of connection between two nodes Given sender of the , the recipients who are frequently addressed are more likely to be the recipient 17

18 SNA for predicting recipients? 1. Importance of a node in the network 1. Number of received s 2. PageRank score of node 2. Strength of connection between two nodes 1. Number of s sent between nodes 2. Number of times two nodes are adressed together 18

19 Part 2: content Statistical Language Models (LMs)! Assign a probability to a sequence of words;! Compute models for different corpora; Used in lots of places; Information Retrieval Machine Translation Speech Recognition 19

20 Language Models Language models as communication profiles 20

21 Language Models Language models as communication profiles 1. Incoming LM (how people talk to user) 21

22 Language Models Language models as communication profiles 1. Incoming LM (how people talk to user) 2. Outgoing LM (how user talks to people) 22

23 Language Models Language models as communication profiles 1. Incoming LM (how people talk to user) 2. Outgoing LM (how user talks to people) 3. Interpersonal LM (how node1 talks with node2) 23

24 Language Models Language models as communication profiles 1. Incoming LM (how people talk to user) 2. Outgoing LM (how user talks to people) 3. Interpersonal LM (how node1 talks with node2) 24

25 Language Models Language models as communication profiles 1. Incoming LM (how people talk to user) 2. Outgoing LM (how user talks to people) 3. Interpersonal LM (how node1 talks with node2) 4. Corpus LM (how everyone talks) 25

26 Why language models? Comparisons between communication profiles: Find nodes with most similar communication 26

27 SNA!! 1. Importance of a node in the network! 3. Strength of connection between nodes Content!! 1. Incoming LM 2. Outgoing LM 3. Interpersonal LM 4. Corpus-based LM!!! 27

28 Approach: time-based t=0 1 , 2 addresses t=1 2 s, 2 addresses t=2 3 s, 4 addresses t=3 4 s, 5 addresses! etc! t=n s, addresses 28

29 At some time interval t Given the , sender, and network Remove recipients from Rank all nodes in the network By computing for each candidate (recipient) node: 1. Importance of candidate 2. Strength of connection between sender and candidate 3. Similarity between sender and candidate LMs 29

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31 Findings: what works for predicting recipients? Importance of node: Number of received s of node! Strength of connection: Number of s between nodes! LM Similarity: Interpersonal LM is most important 31

32 Findings: SNA vs content SNA: SNA signals deteriorate over time SNA signals are most informative on highly active users! content: LM signal improves over time LM signal does worse with highly active users 32

33 Finally Combining Social Network Analysis with Language Modeling is better than doing either. 33

34 Why for E-Discovery Anomaly detection Given a working prediction model; identify unexpected communication Language models for communication For a node, find the most different interpersonal communication Friends/family vs colleagues? Find communication that differs from the corpus-based communication 34

35 Topic Discovery and Tracking in Social Text Streams Zhaochun Ren University of Amsterdam

36 Outline Motivation Challenges Our approach Conclusion & outlook 36

37 Motivation What is Social media? Social media is the social interaction among people in which they create, share or exchange information and ideas in virtual communities and networks 37

38 Social text streams 200 millions tweets posted per day Over 5 billion pages on Facebook Over 55 millions status are made on Facebook 38

39 Question: How can we help users understand social text streams? 39

40 Topic discovery and tracking What is topic discovery and tracking? finding topically related material in streams of data (e.g., newswire and broadcast news)! Methods Local content analysis algorithm (1998) Hidden markov model (2001) Topic models: LDA/pLSA (2003) 40

41 Topic discovery and tracking in social text streams Find important topics in social text streams! Influence of social media user behavior modeling collaborative connections topic drifting on social media 41

42 Outline Motivation Challenges Our approach Conclusion & outlook 42

43 Challenges Topic drifting phenomenon User behavior modeling Volume of social text streams Sparseness on social text streams 43

44 Outline Motivation Challenges Our approach Conclusion & outlook 44

45 Time-aware topic modeling Topic models: Latent dirichlet allocation Each topic can be represented as a finite mixture of words Each document in the corpus can be represented as a mixture of multiple topics Bag of words assumption 45

46 Topic modeling in social text streams Dynamic topic modeling on social streams User behavior modeling on Twitter 46

47 Topic modeling in social text streams Dynamic topic modeling on social streams User behavior modeling on Twitter 47

48 Dynamic topic modeling Social text streams: concept drifting phenomenon! Input data: input! documents X 1 input documentsx 2 input documentsx i!! t 1 t 2 t i Output: topic distribution p(z t) at each time period t 48

49 Application: Hierarchical multi-label classification on social text streams Hierarchical multi-label classification learn a hypothesis function f : X!{0, 1} C from training data {(x (i), y (i) )} D i=1 to predict a y when given input document x Follow T -property some social texts streams belong to to hierarchical multiple labels There are quite cramped trains I think the train will soon stop again because of snow... I really feel like Smullers Communication 200,000 people travel with book as ticket ROOT Product Traveler Personal report Personal experience Retail on station Parking Incident Compliment Complaint Product Experience Smullers 49

50 Hierarchical multi-label classification on social text streams Hierarchical multi-label classification for short documents in social streams Learn from previous time periods, and predict an output when a new document arrives Concept drift phenomenon Document expansion Dynamic topic modeling Structural learning based text classification 50

51 Experimental setup Dataset tweets related to a transportation company from 18th January 2010 to 5th June ,692 tweets posted by 77,161 Twitter users annotations 493 nodes in 13 subsets 51

52 Time-aware topic extraction (1) 1"Train"Schedule 2"winter"chaos 3"sta8on 4"hot"drinks 5"ede>wageningen 1"sta8on 2"winter"chaos 3"chocomel 4"wheel 5"change 1"netherlands 2"train 3"bomb 4"NS"company 5"police 1"train 2"train"cancel 3"snow"fall 4"froze 5"clumsy"work 1"bomb 2"NS 3"pains 4"police 5"train 52

53 Time-aware topic extraction (2) macro F #days C SSVM LTC SSVM GTC SSVM 53

54 Topic modeling in social text streams Dynamic topic modeling on social streams User behavior modeling on Twitter 54

55 Tweet Propagation Model Key idea 55

56 Tweet Propagation Model Candidate tweets User s own tweets RT t1 θ u,t1 RT t2 θ u,t2 time time Probability of user s interests at each time period Probability of topics at each time period 56

57 Application: personalized time-aware tweets summarization Time-aware tweets summarization Select the most representative tweets for each time period as summary Personalized time-aware tweets summarization Summary needs to be relevant to user s interests Data preprocessing Tweets propagation model Document summarization: sentence extraction 57

58 Overall performance 40 tweets per period Metrics TPM-A TPM-T TPM-S UBM TLDA AT TF-IDF Centroid Lex-R R R R-W tweets per period Metrics TPM-A TPM-T TPM-S UBM TLDA AT TF-IDF Centroid Lex-R R R R-W

59 Outline Motivation Problem definition Our approach Conclusion & outlook 59

60 Conclusion Dynamic topic modeling in social text streams A new topic model for synchronous tracking topics and user s interests Experiments on industrial dataset demonstrate the effectiveness of our proposed method 60

61 Future work Contrastive aspect extraction Large-scale topic modeling Parallel processing to enhance the efficiency Active learning to optimize the period size Continuous time periods 61

62 Thanks! Zhaochun Ren David 62