Annotating Spatial Containment Relations Between Events. Kirk Roberts, Travis Goodwin, and Sanda Harabagiu
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1 Annotating Spatial Containment Relations Between Events Kirk Roberts, Travis Goodwin, and Sanda Harabagiu
2 Problem Previous Work Schema Corpus Future Work
3 Problem Natural language documents contain a large number of event mentions Many resources tie events to their location and participants e.g., FrameNet, PropBank, NomBank, ISO-Space However, very few events have explicit spatial information Only TODO% of events have a location in our data
4 Problem
5 Problem Heuristic: choose closest location (token-wise) TODO: cite Completely unprincipled Ignores the complex relationship that locations have with events Events may occur within a location, include a location, occur near a location, etc.
6 Problem Rafiq Hariri submitted his resignation during a 10-minute meeting with the head of state at the Baabda presidential palace. submitted TLINK meeting Rafiq Hariri Baabda presidential palace
7 Problem As Egyptian columns retreated, Israel s aircraft attacked them, using napalm bombs. The attacks destroyed hundreds of vehicles and caused heavy casualties in Sinai. retreated attacked attacks caused Egyptian columns Sinai
8 Problem Previous Work Schema Corpus Future Work
9 SpatialML Identifies locations (PLACE) trajectories (PATH) containment relations (LINK) Disambiguates toponyms (e.g., Paris ) PLACE PLACE PLACE PLACE Royal Plaza is in Mong Kok, north of Tsim Sha Tui, Kowloon. LINK PATH LINK
10 ISO-Space Identifies Events (EVENT and MOTION) Location containment relations (EVENT_LOCATION and EVENT_PATH) Relations between locations (QSLINK and S_FUNCTION) MOTION LOCATION John drove to Boston. EVENT PATH
11 Missing Piece Detailed representations of event locations, but very few events have explicit locations Spatial information is often stuck in silos Solution: event-event relations that describe spatial relationships in much the same way as SpatialML describes location-location relations ISO-Space describes event-location relations E.g., a spatial analog of TimeML that, but designed with implicit relations in mind
12 Problem Previous Work Schema Corpus Future Work
13 Schema Rafiq Hariri submitted his resignation during a 10-minute meeting with the head of state at the Baabda presidential palace. participant submitted Rafiq Hariri contains meeting location Baabda presidential palace
14 Schema Containment relations derived from RCC-8 while accounting for common ambiguities in natural language Disconnected (DC) Externally Connected (EC) Tangential Proper Part (TPP) Tangential Proper Part Inverse (TPPi) Non-tangential Proper Part (NTTP) Non-tangential Proper Part Inverse (NTTPi) Partially Overlapping (PO) Equal (EQ) Different Near Contains Overlaps Same
15 Schema Event-Event (containment) 5 sub-types Event-Participant 3 sub-types Event-Location 3 sub-types
16 Example As Egyptian columns retreated, Israel s aircraft attacked them, using napalm bombs. The attacks destroyed hundreds of vehicles and caused heavy casualties in Sinai. attacked retreated contains same contains caused participant Egyptian columns attacks Sinai location
17 Example The soldier sent his ballot for the Maine election. participant (P R) soldier sent participant ballot overlaps election Maine location
18 Example
19 Example
20 Example
21 Example
22 Problem Previous Work Schema Corpus Future Work
23 Corpus Subset of SpatialML corpus 160 newswire documents Gold-standard locations pre-annotated Automatic tagging for events and candidate participants (person/org entities) Difficult annotation task, fairly low agreement Several rounds of annotation/inspection
24 Corpus Containment relation distribution:
25 Simple Baseline To approximate the difficulty of the task, we evaluated with an SVM classifier and a simple feature set: EW: Both event words (submitted::meeting) EL: Both event lemmas (submit::meeting) WB: Words between events (his, resignation, during, a, 10-minute) HN: Hypernyms of events (refer::gathering, send::gathering )
26 Simple Baseline Relation Exists Feature Set P R F 1 EW EL WB HN EW + HN EL + HN WB + HN EW + WB + HN EL + WB + HN EW + EL + WB + HN
27 Simple Baseline Relation Type (5-way) Feature Set % EW 58.3 EL 57.7 WB 52.1 HN 54.9 EL + EW 57.9 WB + EW 53.1 HN + EW 54.8
28 Problem Previous Work Schema Corpus Future Work
29 Future Work For annotation: Finish annotation effort (abt. 2/3 complete) Integration with other spatial information standards (e.g., ISO-Space)
30 Future Work For classification, it is clear that basic NLP features are insufficient Further integration of event structures to represent complex event semantics Events narratives (Chambers & Jurafsky, 2008), (Bejan, 2008) Event coreference Discourse relations Relative spatial bounds of events (e.g., a football game is larger than a pass or kick)
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