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STS: Temporal and Spatial Constraints on Text Similarity

James PustejovskyBrandeis University

March 13, 2012

Measuring Similarity

• Objects• Events

Object similarity is a function of:

1. Sortal correlation2. Temporal proximity3. Spatial proximity

• the Latin Quarter of the 1920s– the 5th Arrondissement in 1929– Paris in 1925– The Left Bank in the early 20th Century

Event similarity is a function of:

1. Predicative similarity2. Participant correlation3. Temporal proximity4. Spatial proximity

cf. Kim (1993), Davidson (1980), Lewis (1986)

Brandeis CS114-2012 Pustejovsky

Event Similarity

a. Mary visited John in Boston on Tuesday.b. The woman/she saw her husband in Copley Square yesterday.

• Sim(P1,P2): visit vs. see

• Sim(Subj1,Subj2): Mary vs. the woman

• Sim(Obj1,Obj2): John vs. her husband

• Sim(Loc1,Loc2): Boston vs. Copley Square

• Sim(Time1,Time2): Tuesday vs. yesterday


Predicative Similarity

• Lexical resources• LSA• Vector-based models


Argument Alignment

• Semantic Role Labeling +

• Sortal Similarity


Temporal Similarity

• Normalization– Map to standardized ISO-TimeML format

• Referencing– Reference relative to local temporal values

Val(Tuesday) = Val(yesterday)


Spatial Similarity

• Normalization– Map to standardized ISO-Space format

• Referencing– Reference relative to accessible spatial values

Val(Copley_Sq) Spatial-IN Val(Boston)


Temporal Issues

• Subsumption in anchoring– The bombing occurred Monday morning.– The bombing occurred Monday.– The bombing occurred last week.

Motivation for time and event markup

• Natural language is filled with references to past and future events, as well as planned activities and goals;

• Without a robust ability to identify and temporally situate events of interest from language, the real importance of the information can be missed;

• A Robust Annotation standard can help leverage this information from natural language text.

Temporal Awareness in Real Text

• The bridge collapsed during the storm but after traffic was rerouted to the Bay Bridge.

• President Roosevelt died in April 1945 before– the war ended. (event happened)– he dropped the bomb. (event didn’t happen)

• The CEO plans to retire next month. • Last week Bill was running the marathon when he

twisted his ankle. Someone had tripped him. He fell and didn't finish the race.

Current Time Analysis Technology

• Document Time Linking– Find the document creation time and link that to

all events in the text;

• Local Time Stamping– find an event and a “local temporal expression”,

and link it to that time;

Document Time Stamping

April 25, 2010• President Obama paid tribute Sunday to 29

workers killed in an explosion at a West Virginia coal mine earlier this month, saying they died "in pursuit of the American dream." The blast at the Upper Big Branch Mine was the worst U.S. mine disaster in nearly 40 years.Obama ordered a review earlier this month and blamed mine officials for lax regulation.

Document Time Stamping:



Identify which Events Should be Ordered

• The annotation specification should specify a kernel of events and time expressions to be annotated.

• Anchoring relations between events and times depend on genre, style, and register.

• Ordering relations between events depend largely on discourse relations in the text.

Creation vs. Narrative Time

• Document Creation Time– when the utterance is made (speech time)

• Narrative Time– when the event occurs

Genre, Style, and Register

• Participants• Relations among participants• Channel• Production Circumstances• Setting• Communicative Purpose• Topic

Genre, Register, and Style

• Help distinguish text types in order to better characterize the information structure of the text

• Example, news wire vs. news article– narrative time (NT) is a function of

publication/creation frequency.

Narrative Time

• Identifies the temporal interval of the events being described in the text. – Document Narrative Time: set by text-genre– Current Narrative Time: shifts through the text

Document Time Stamping: for real



Narrative Container


workers killed in an explosion at a West Virginia coal mine earlier this month, saying they died "in pursuit of the American dream." The blast at the Upper Big Branch Mine was the worst U.S. mine disaster in nearly 40 years. Obama ordered a review earlier this month and blamed mine officials for lax regulation.

Time Stamping: the good, bad, …

✓• ☺Set up a meeting on Tuesday with EMC.

✓• ☺Franklin arrives tomorrow from London.

✗• ☹ Franklin arrives on the afternoon flight from

London tomorrow. ✗

• ☹☹Most people drive today while talking on the phone.

ISO-TimeML Enables Temporal Parsing

• A new generation of language analysis tools that are able to temporally organize events in terms of their ordering and time of occurrence

• These tools can be integrated with visualization, summarization, question answering, and link analysis systems to help analyze large event-rich information spaces.

ISO-TimeML Provides elements to:

• Find all events and times in newswire text• Link events to the document time and to local

times• Order event relative to other events• Ensure consistency of the the temporal

relations

ISO-Space

• Capture the complex constructions of spatial language in text

• Provide an inventory of how spatial information is presented in natural language

• ISO-Space is not designed to provide a formalism that fully represents the complexity of spatial language

Applications of ISO-Space

• Building a spatial map of objects relative to one another.• Reconstructing spatial information associated with a sequence

of events.• Determining object location given a verbal description.• Translating viewer-centric verbal descriptions into other

relative descriptions or absolute coordinate descriptions.• Constructing a route given a route description.• Constructing a spatial model of an interior or exterior space

given a verbal description.• Integrating spatial descriptions with information from other

media.

Semantic Requirements for Annotation

• Fundamental distinction between the concepts of annotation and representation– Based on ISO CD 24612 Language resource management -

Linguistic Annotation Framework (Ide and Romary, 2004)

• Distinguish between abstract syntax and concrete syntax– Concrete Syntax XML encoding– Abstract Syntax Conceptual inventory and a set of

syntactic rules defining the combination of these elements

Spatial Expressions

• Constructions that make explicit reference to the spatial attributes of an object or spatial relations between objects

• Four grammatically defined classes:– Spatial Prepositions and Particles: on, in, under, over, up,

down, left of– Verbs of Position and Movement: lean over, sit, run, swim,

arrive– Spatial Attributes: tall, long, wide, deep– Spatial Nominals: area, room, center, corner, front, hallway

Spatial Relations

• Topological:– In, inside, touching, outside

• Orientational (with frame of reference):– Behind, left of, in front of

• Topo-metric:– Near, close by

• Topological-orientational:– On, over, below

• Metric:– 20 miles away

Frames of Reference (Levinson, 2003)

• Absolute– The lake is north of the city.

• Relative– The book is to your left. – The tree is between the Pru and the Monitor.

• Intrinsic– There’s a ball in front of the car.– The tree is behind the bench.

Frames of reference

• The tree to the left of the entrance• The steps in front of me/the entrance

ISO-Space 1.4

• Spatial Relations are split into 4 types:– Topological (QSLink)– Relational (OrientLink)– Movement (MoveLink)– Measurement (MLINK, from TimeML)

• Spatial Relations are identified with role labels, include Figure and Ground

• SPATIAL_NAMED-ENTITY

Conclusion: Measuring Semantic Similarity

• Normalizing temporal and spatial expressions• Developing standardized specifications

contribute towards corpora for training and evaluation for such normalization

• Cases in point:– ISO-TimeML (ISO adopted)– ISO-Space (in development)