Building a Timeline Action Network for Evacuation in Disaster

Transcription

1 Building a Timeline Action Network for Evacuation in Disaster The-Minh Nguyen, Takahiro Kawamura, Yasuyuki Tahara, and Akihiko Ohsuga Graduate School of Information Systems, University of Electro-Communications, Japan

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3 Objective Building a service, which recommends action sequence based on social sensors

4 Goal: Timeline Action Network train was stopped 15:13:00 waited a taxi but failed 17:13:00-1 took a bus to go to Shibuya station 18:20:00 walked from Shinjuku station 19:52:09 Success: 9 walked from Shibuya station 20:20:00 received mobile battery at Bic Camera 20:40:00 took refuge 20:22:09 Success: 8

5 Challenge 1: How to extract human activity? In the earthquake, phone lines got stuck, and then twitter was used to exchange information about evacuation, traffic (33 million tweets/day) However, tweets are noisy and syntactically incorrect Challenge 2: How to represent action sequence? To recommend the action sequence, the meaning of the network needs to be understood by both of computer and the human user Challenge 3: How to predict missing activity? Users do NOT post all of their activities in real-time 17:00 20:00 23:00 Roppongi act 1? act 4 Where? Shinjuku

6 Approach 1: Definition of Activity Attributes Attributes to express the human activity time (when) location (where) Relation 1 transition 2 cause object (what) actor (who) Label next act (how) becauseof Tanaka came back home after escaping from the earthquake earthquake escape what who Tanaka next home what come back

7 Approach 1: Difficulty of extraction Frequency or co-occurrence miss most of activities Frequency of activities related to Akihabara electric town Co-occurrence frequency of action and object Consider activity extraction as a sequence labeling problem Output (label) B-What O B-Action O Input ipad を買うよ ipad buy (buy an ipad) B: Begin a phrase, I: Inside a phrase, O: not in a phrase In labeling problem, CRF outperform both MEMMs and HMMs on a number of tasks: text chunking, named entity recognition etc.

8 Approach 1: Process of Activity Extraction 1 NLP method: -deep linguistic parser -syntax patterns -Google Map API parable sentences the other sentences Preprocessing: 3 -remove noise data -convert to simpler sentences Training data 2 Learning model Extract labels of activity attributes 4 Add more training data Feature model of training data Make training data for twitter Extract activity

9 Approach 1: Making training data for twitter easy parsing sentences (initial set of sample data) and twitter and twitter API

10 Approach 2: Design of Timeline Action Network Based on Web Ontology Language (OWL) RDF/N3: compact and readable alternative to RDF s XML Inherit external geo: vcard: tl: < C, I, L are class, instance, label respectively

11 Approach 2: Example of semantic description The train has stopped at Akihabara Station at 16:13:00. :stop a :ActClass ; rdfs:label "stop"@en. :akihabara_station a :TrainStationClass ; rdfs:label Akihabara ; vcard:region ; vcard:locality "Chiyoda-ku"@en ; vcard:street-address " Sotokanda"@en ; geo:lat ; geo:long :act_01 a :ActionClass ; :act :stop ; :what :"train"@en ; :where :akihabara_station ; tl:start T16:13:00^^xsd:dateTime.

12 Approach 1&2: Building of Timeline Action Network Using #jishin (#earthquake) tag to extract activity sentences which relate to earthquake e.g. Earthquake M9.0 was just occurred ( :47) I am taking refuge at Akihabara ( :10) Extract activity attributes Activity ID (Who, Act, What, Where, When) act01 (Null, occur, earthquake M9.0, Null, :47) act02 (I, take refuge, Null, Akihabara, :10) Convert to RDF/N3 :act01 a :ActionClass ; :act :occur ; :what "earthquake M9.0"@en ; tl:start " T14:47:00"^^xsd:dateTime. act02 a :ActionClass ; :act :take_refuge ; :where :Akihabara ; tl:start " T15:10:53"^^xsd:dateTime.

13 Approach 3: Prediction of Missing Activity Can act = {act 1, act 2,.., act t, } is set of candidate actions at t t time P ua act t? 1 based on similar users 2 based on action sequence probability of act t Correspond to user-based collaborative filtering (CF) Correspond to item-based CF We can use CF approach to calculate probability of the active user u a did action act t at time t (Pu a act t )

14 Approach 3: Difficulty of Prediction Traditional CF is trying to recommend interested items on EC site for users Our work is trying to predict missing action in real-world Complexity point of view items on internet actions in real-world Dependence 1 variable 4 variables (act, object, time, location) location NO YES transition weak strong goal NO YES (e.g. want to evaluate) Continuity NO YES It is NOT suitable to use traditional CF for our work

15 Approach 3: Prediction based on user similarity Assumption: (a) similar users also did before/after actions (b) if users had the same goal (e.g. wanted to evacuate in Shinjuku), then they had the same action patterns (c) users did the same action if they were in the same location Similarity between u j & u a : (a) did before/after actions or NOT S(u j,u a ) = βdid({a before,l before },{a after,l after }) + γ Target(a t,l t ) + (1 β γ)samelocation(l) (b) had the same goal or NOT (c) were in the same location or NOT

16 Approach 3: Prediction based on Action Sequence Assumption: Action depends on (a) time (b) location (c) before/after action a before act t a after Probability of act t at time t : (c) frequency of a before act t (c)frequency of act t a after P(act t ) = ρ a + ρ t {F(a F(act before t act,t) + (1 ρ t ) + a ρ F(act t t )F(act t a,l) after )} (a) frequency of act t at time t (b) frequency of act t in location l

17 Approach 3: Prediction based on execution time Assumption: Same actions need same execution time active user u a before action? after action time time_1 similar user u j before action time_b act 1 act 2 after action time time_t t j1 t jb t ab t t j2 tj Probability of act 1 of user u j : time_t time_b T(u j,act1) = 1 time_1 diff if (0 diff else diff < 0 time_1) b diff = time_t time_b

18 Approach 3: Handling Minority Action (1/2) Assumption: Minor actions which were not frequent but successful, should be weighted E.g. it was a good decision when staying at company feedback minor action Action stay at company should be weighted, using NLP to extract feedbacks

19 Approach 3: Handling Minority Action (2/2) Probability of u a who did a successful action act t : A. Proportional to percentage of successful actions of u a B. Proportional to percentage of good feedbacks of act t A. B. action success number of success act before 0 0 act act act act after 0 0 f(u a ) = Success(act 2 ) = DidSuccess ua act t = f(u a )*Success(act t )

20 Approach 3: Prediction of Missing Activity Combination of CF + NLP : NLP P * u a act t = f(u a )*Success(act t ) + CF P ua act t P u a act t ω(u,act j j = 1,L = α P(actt ) (1 α) P(act ) 1 user based i t ) S(u L j,u a ) T(u 2 action based 3 execution time based j,act ) + 0 α, β, γ, ρ a, ρ t 1 L is number of all users similar to u a ω(u j, act t ) is a weighting factor t

21 Evaluation: Data set 132,244 tweets which were posted by users in Tokyo from 2011/03/11 to 2011/03/12 416,463 tweets which related to the massive Tohoku earthquake

22 Evaluation: Activity Extraction method activity actor act object time location NLP 81.17% 86.32% 98.13% 84.14% 87.96% 88.25% Precision CRF 57.89% 72.79% 82.98% 67.01% 76.40% 80.20% CRF w/ twitter data 73.21% 82.25% 97.11% 81.23% 80.04% 82.11% NLP 23.86% 26.38% 28.87% 24.77% 26.20% 26.02% Recall CRF 51.13% 69.13% 90.23% 62.11% 73.51% 77.67% CRF w/ twitter data 66.54% 80.11% 93.18% 76.57% 79.75% 81.02% NLP 36.88% 40.41% 44.61% 38.27% 40.37% 40.19% F-measure CRF 54.30% 70.91% 86.45% 64.47% 74.93% 78.91% CRF w/ twitter data 69.72% 81.17% 95.10% 78.83% 79.89% 81.56% NLP has high precision but low recall F-measure is improved by adding more training data and doing pre-processing NLP: use of NLP tools alone CRF: use of CRF, but not trained by twitter data

23 Evaluation: Prediction of Missing Activity Repeat the followings for action data of 3,900 users 10 times 1. Randomly select 39 users (10%) 2. Randomly delete activity data of the above users 3. Try to predict the deleted activity from the other data Method action location action & location Baseline 31.48% 43.09% 27.56% CF+NLP 69.23% 76.92% 43.59% Each attribute has high accuracy, but Accuracy of their combination is still low, and requires the introduction of combinational prediction metrics Baseline: Look up the most similar user, and predict missing activity only from the user

24 Application: Timeline Action Network (1/2) Android App. which recommend action sequence for evacuation based on the timeline action network. Input(time, location) Output (action patterns)

25 Application: Timeline Action Network (2/2)

26 Conclusion Objective: Building a service, which recommends action sequence based on social sensors Challenge: 1. How to extract human activity? 2. How to represent action sequence? 3. How to predict missing activity? Approach: 1. Human Activity Extraction from twitter 2. Building of Timeline Action Network 3. Predict Missing Activity Future Issue: Not only the earthquake, we would apply this method to other disasters and business use, such as typhoon, train accident, travel recommendation, and behavior-based marketing, etc.