Improving String Processing for Temporal Relations Tim Fernando - PowerPoint PPT Presentation

Improving String Processing for Temporal Relations Improving String Processing for Temporal Relations Tim Fernando David Woods ADAPT Centre Computational Linguistics Group Trinity Centre for Computing and Language Studies School of Computer Science and Statistics Trinity College Dublin, Ireland ISA-14, August 25th 2018

Improving String Processing for Temporal Relations Introduction Introduction Q. What’s this talk about?

Improving String Processing for Temporal Relations Introduction Introduction Q. What’s this talk about? A. Representing temporal information compactly for reasoning and processing.

Improving String Processing for Temporal Relations Introduction Introduction Q. What’s this talk about? A. Representing temporal information compactly for reasoning and processing. Example “John slept through the fire alarm last Tuesday.” lt lt , js lt , js , fa lt , js lt

Improving String Processing for Temporal Relations Introduction Motivation Ordering events and times is crucial in natural language understanding.

Improving String Processing for Temporal Relations Introduction Motivation Ordering events and times is crucial in natural language understanding. Providing a useful way to both viualise a document’s temporal structure, and perform inference with the same framework.

Improving String Processing for Temporal Relations Introduction Motivation Ordering events and times is crucial in natural language understanding. Providing a useful way to both viualise a document’s temporal structure, and perform inference with the same framework. Assisted annotation for TimeML or similar schemas.

Improving String Processing for Temporal Relations Introduction TimeML ISO-TimeML What is TimeML?

Improving String Processing for Temporal Relations Introduction TimeML ISO-TimeML What is ISO- TimeML?

Improving String Processing for Temporal Relations Introduction TimeML ISO-TimeML What is ISO- TimeML? An ISO standard markup language for annotating temporal information in texts.

Improving String Processing for Temporal Relations Introduction TimeML Why TimeML? Widely known, and ISO standard. The TLINK elements map well to Allen’s interval relations. The TimeBank corpus (183 documents manually annotated with TimeML).

Improving String Processing for Temporal Relations Introduction TimeML TimeML Example Example John taught 20 minutes every Monday. Example from http://www.timeml.org/publications/timeMLdocs/timeml_1.2.1.html

Improving String Processing for Temporal Relations Introduction TimeML TimeML Example Example John taught 20 minutes every Monday. Example from http://www.timeml.org/publications/timeMLdocs/timeml_1.2.1.html

Improving String Processing for Temporal Relations Introduction TimeML TLINKs Example Example John taught 20 minutes every Monday. <TLINK timeID="t1" relatedToTime="t2" relType="IS INCLUDED"/> <TLINK eventInstanceID="ei1" relatedToTime="t1" relType="DURING"/>

Improving String Processing for Temporal Relations Introduction TimeML TLINKs Example Example John taught 20 minutes every Monday. <TLINK timeID="t1" relatedToTime="t2" relType="IS INCLUDED"/> <TLINK eventInstanceID="ei1" relatedToTime="t1" relType="DURING"/> Several ways to represent this information.

Improving String Processing for Temporal Relations Introduction TimeML TLINKs in a Directed Graph Example John taught 20 minutes every Monday.

Improving String Processing for Temporal Relations Introduction TimeML TLINKs using T-BOX Example John taught 20 minutes every Monday.

Improving String Processing for Temporal Relations Introduction TimeML TLINKs as Strings Example John taught 20 minutes every Monday. t 2 t 1 , t 2 ei 1 , t 1 , t 2 t 1 , t 2 t 2

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data Sequences of Sets-as-Symbols Strings of n sets α 1 α 2 · · · α n are used to represent n moments of time.

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data Sequences of Sets-as-Symbols Strings of n sets α 1 α 2 · · · α n are used to represent n moments of time. Each set α i contains exactly those fluents (temporal propositions, treated as intervals) which are true at moment i . e.g. { a }{ a , b }{ b }

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data Sequences of Sets-as-Symbols Strings of n sets α 1 α 2 · · · α n are used to represent n moments of time. Each set α i contains exactly those fluents (temporal propositions, treated as intervals) which are true at moment i . e.g. { a }{ a , b }{ b } Sets are drawn as boxes, so strings can be read like comic strips, snapshots of film, or timelines. e.g. a a , b b

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data Comic Strips Image from The National Archives UK

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data An Intertial World Note! A fluent occurring in multiple boxes does not imply a longer duration.

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data An Intertial World Note! A fluent occurring in multiple boxes does not imply a longer duration. For example, a is equivalent in interpretation to a a a , though the latter string is said to feature stutter .

Improving String Processing for Temporal Relations Introduction Strings for Temporal Data An Intertial World Note! A fluent occurring in multiple boxes does not imply a longer duration. For example, a is equivalent in interpretation to a a a , though the latter string is said to feature stutter . We can remove stutter through a block-compression operation:  if length ( s ) ≤ 1 s  c ( α s ′ ) if s = αα s ′ c ( s ) := b b if s = αα ′ s ′ with α � = α ′ c ( α ′ s ′ ) α b 

Improving String Processing for Temporal Relations Introduction Allen’s Interval Relations Allen Relations Allen treats intervals as primitive for events, not start/end points. Allen’s Interval Relations form the basis of TimeML’s TLINK relation types. These are easily transformable to strings.

Improving String Processing for Temporal Relations Introduction Allen’s Interval Relations Allen Relations as Strings R − 1 a R − 1 a ′ a R a ′ S R ( a , a ′ ) S R − 1 ( a , a ′ ) R a before a ′ a ′ a after a ′ a ′ a a < > a meets a ′ a ′ a met by a ′ a ′ m a mi a a overlaps a ′ a , a ′ a ′ a overlapped by a ′ a ′ a ′ , a o a oi a d a during a ′ a ′ a , a ′ a ′ di a contains a ′ a ′ , a a a a starts a ′ a , a ′ a ′ a started by a ′ a ′ , a s si a a finishes a ′ a ′ a , a ′ a finished by a ′ a ′ , a f fi a a equals a ′ a , a ′ = Allen’s interval relations as strings.

Improving String Processing for Temporal Relations Introduction Allen’s Interval Relations Extraction of ARs as Strings from TimeML https://www.scss.tcd.ie/~dwoods/isa14/

Improving String Processing for Temporal Relations Introduction Superposition Combining Strings Superposition allows us to condense the information from multiple strings into a more compact form.

Improving String Processing for Temporal Relations Introduction Superposition Combining Strings Superposition allows us to condense the information from multiple strings into a more compact form. The simplest version of the operation is just componentwise union of two strings of equal length: α 1 α 2 · · · α n & α ′ 1 α ′ 2 · · · α ′ n := ( α 1 ∪ α ′ 1 )( α 2 ∪ α ′ 2 ) · · · ( α n ∪ α ′ n )

Improving String Processing for Temporal Relations Introduction Superposition Combining Strings Superposition allows us to condense the information from multiple strings into a more compact form. The simplest version of the operation is just componentwise union of two strings of equal length: α 1 α 2 · · · α n & α ′ 1 α ′ 2 · · · α ′ n := ( α 1 ∪ α ′ 1 )( α 2 ∪ α ′ 2 ) · · · ( α n ∪ α ′ n ) Example (Basic Superposition) a b c & a a d = a a , b c , d

Improving String Processing for Temporal Relations Introduction Superposition Asynchronous Superposition We handle strings of unequal length by introducing stutter using an inverse block compression, such that the operands are padded to the point that basic superposition is possible.

Improving String Processing for Temporal Relations Introduction Superposition Asynchronous Superposition We handle strings of unequal length by introducing stutter using an inverse block compression, such that the operands are padded to the point that basic superposition is possible. Noting that every string s ′ ∈ b c − 1 b c ( s ) is equivalent under compression, we define asynchronous superposition : s & ∗ s ′ := { b c ( s ′′ ) | s ′′ ∈ b c − 1 b c − 1 b c ( s ′ ) } c ( s ) & b