Temporal information extraction: Reasoning with events based on their descriptions

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Temporal information extraction: Reasoning with events based on their descriptions. Leon Derczynski University of Sheffield. Introduction. Background Anchoring events Reasoning about events Representing temporal data Evaluating annotations. Background. Why bother?
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Temporal information extraction:Reasoning with events based on their descriptionsLeon DerczynskiUniversity of SheffieldIntroduction
  • Background
  • Anchoring events
  • Reasoning about events
  • Representing temporal data
  • Evaluating annotations
  • Background
  • Why bother?
  • Temporal information affects everything described by language.
  • The world is in a state that changes with time.
  • Not all assertions made in written text are true together.
  • Temporal information shows which sets of data can concurrently be true.
  • Tense and temporal models
  • Zeno Vendler (1957)
  • “Verbs and Times”
  • Hans Reichenbach (1947)
  • “The tenses of verbs”
  • James Allen (1983)
  • “Maintaining knowledge about temporal intervals”Vendler
  • Vendler verb classification:
  • Verb instances fall into one of four groups:
  • Stative: a persistent state (“John sits”)
  • Activity: lasts for a finite period (“Bob ran for an hour”)
  • Accomplishment: takes a finite period, and culminates (“Kate climbed the hill in five minutes”)
  • Achievement: Instantaneous finishing events (“Lucy reached the top of Everest”)
  • Tests are provided to see which group a verb sense fits in.
  • Reichenbach
  • Reichenbach model of verb tenses:
  • Speech time: when the words were uttered.
  • Event time: when the described event occurred.
  • Reference time: like a viewpoint.
  • “The cat will break the door” – ST=RT, ET in the future“The cat will have broken the door” – ST = present, ET in the future, RT looks back onto ET
  • Allows simplistic description of any phrase.
  • Tracking reference time is sometimes very helpful:
  • “When John comes home, I will have gone” In this case, when describes a reference time for the whole sentence.Allen
  • Interval logic:
  • All events are described as intervals, with start and end points.
  • Interval relation types are defined (before, includes, starts…).
  • A table for inferring about interval relations is given.
  • E.g.: AbeforeB, AincludesD:
  • Before stipulates that A’s endpoint is before B’s start.
  • We can infer DbeforeB.
  • Anchoring events
  • Introduction to event anchoring
  • Dealing with named weekdays
  • TEA – an implemented anchoring system
  • Problems
  • Anchoring events
  • Fixing information from text to a timeline.
  • Calendrical time is a common reference, given a calendar.
  • Expressions describing a time are sometimes referred to as TIMEXs.
  • Once identified, a TIMEX may be normalised to a fully specified date or interval.
  • Named entity recognition, finite state grammars and machine learning have all been used to identify these expressions.
  • Appropriate granularity should be chosen.
  • Weekday references
  • The English week has seven day names.
  • A single day name is often deemed sufficient reference for a human:
  • “I’ll see you next Tuesday”
  • “Monday, and the markets are buzzing”
  • To anchor a weekday, given ST and a day name, we need to choose direction from ST, and optionally distance.
  • Baldwin: an inclusive 7-day sliding window, centred on today.
  • Mani & Wilson: find controlling verb’s tense and use this to determine direction.
  • Tense estimation: check PoS of sentence tokens for VBD; if found, assume backwards.
  • Dependency-based: use Stanford parser to find controlling verb.
  • Mazur & Dale - “What’s the Date?” (2008)Generic vs. specific
  • Some expressions, that look like TIMEXs, should not be normalised.
  • “Today” can mean:
  • the 24-hour period containing ST and bounded by midnights.
  • Modern times, or a change of frame of reference:
  • “In Victorian times, ladies wore long dresses. Today, modern fashions do not dictate a single length.”
  • This second idea is not restricted to the period from 00:00 to 23:59 GMT on Thursday 7th May 2009!
  • As 90% of uses in some texts are specific 1, some systems choose to accept a 10% error rate.
  • Features based on local words can help distinguish generic from specific, but below this baseline accuracy. 2
  • 1: Han, Gates & Levin – “From Language to Time: A Temporal Expression Anchorer” (2006)2: Mani & Wilson – “Robust Temporal Processing of News” (2000)TEA
  • Temporal Expression Anchorer: Han, Gates & Levin at CMU.
  • Calendar used as time ontology, dealing with various levels of granularity.
  • Processes TCNL (Time Calculus for Natural Language).
  • Identifies temporal expressions in input, and associates TIMEXs with their textually nearest verb.
  • Absolute and relative expressions are evaluated using TCNL:
  • “Friday last week” is split, into “Friday” and “last week”
  • {fri} + {now - |1week|} = {fri,{now - |1week|}} = {now - |1fri|}
  • Constraint satisfaction based on a calendar model narrows the possible set of absolute dates.
  • Determining event durations
  • Given some normalised expressions, knowing event durations can greatly increase our reasoning ability.
  • Data can be taken from human annotators.
  • Determining a typical event duration is difficult:
  • “The dog ran up the hill”
  • “Linda had finished her cleaning”
  • This results in low inter-annotator agreement.
  • A simplified approach would allocate durations into two classes: shorter or longer than a day.
  • Possible to classify events this simply with 76% accuracy, using hypernym and local word PoS features.
  • Pan, Mulkar & Hobbs – “Learning Event Durations from Event Descriptions” (2006)Reasoning about events
  • Introduction
  • Temporal closure
  • Minimal notations and temporal inference
  • Help from linguistic models
  • Reasoning about events
  • Annotations often only describe a subset of a document’s temporal information, perhaps as a number of labelled events and times.
  • An annotation may also include some links between pieces of temporal information.
  • It is possible to infer data about relations between points, given a set of rules or logic, and some existing relations.
  • It is also possible to add detail and boundaries to an annotation based on linguistic features of the source text.
  • This ability to reason about events saves human annotators work, and allows us to maximise the available descriptions from their efforts.
  • Temporal closure
  • A temporal closure can be thought of as a graph:
  • Times and events are node; relations are edges.
  • Every time and event is connected to every other.
  • E.g.
  • t1 is Tuesday 5th May 2009
  • e1 is hearing this talk
  • We can say: t1beforee1, thus giving a type to this relation.
  • A temporal closure includes relations between every node in the graph.
  • This can lead to very large amounts of data for only a moderate-sized document.
  • Minimal annotations
  • It is rare for every relation (graph edge) to be annotated. We can infer some relations:
  • (t1beforee1) ^ (e1beforee2) => (t1beforee2)
  • Inference can be used to complete a closure without specifying every relation’s type.
  • When this applies, and no more relations can be removed, we have a minimal annotation.
  • For example:
  • Three nodes: e4, e5, e6
  • Closure has 3 possible relations
  • A minimal graph may just say:
  • (e4aftere5)
  • (e5simultaneouse6)
  • To infer the closure, we simply need to add:
  • (e4aftere6), or (e6beforee4)
  • Relation inference
  • Allen’s interval logic describes 13 relationships, and provides a transitivity table for inferring a relation given two related ones.
  • Some inconsistent labellings are possible.
  • Backtracking over the initial graph should detect these cases.
  • A set of ten inference rules can be used:
  • Allen’s 13 relations are reduced to just 3, including some reversal of parameters.
  • Only before, simultaneous and includes are used
  • e9aftere10 => e10beforee9
  • These rules can be iteratively added to an agenda and used to reason with a database of approved relations.
  • For small graphs (< 2000 edges) we can assign types to around 10% of relations, given a human annotation.
  • Applying Reichenbach
  • Reference time can provide a boundary on an event.
  • “John had eaten all the pies”
  • Event 1 = eating
  • ET – RT – ST
  • “John had eaten all the pies when Annika arrived”
  • Event 2 = arriving
  • Reference time is the same across the sentence.
  • ET – RT = ET2 – ST
  • Because we know that RT is after ET and equal to ET2, we can specify three temporal relations:
  • e1beforee2
  • e1beforeST
  • e2beforeST
  • Having a model for tenses allows us to confidently add relations to a temporal graph of a discourse.
  • Representing temporal data
  • Introduction
  • TIMEX and TimeML
  • TCNL
  • T-BOX
  • Representing temporal data
  • Once we can identify temporal information, we need to store this information.
  • Temporal information is rich, and favours a format that can capture it well.
  • Aspect, polarity, tense, part of speech
  • Event class, event frequency
  • Hints about reference, speech and event time
  • Notation languages are available both for storing and working with this data.
  • These languages are new (under a decade old), and possibly not yet mature.
  • TIMEX
  • Standard for describing a time-specific expression.
  • Evolved through the MUC conferences and TERN, through TIMEX, TIMEX2 and TIMEX3.
  • TIMEX3 is currently used as the means of describing absolute times in TimeML.
  • <TIMEX3 tid="t43" type="DATE" value="1989-10-30" temporalFunction="false" functionInDocument="CREATION_TIME">10/30/89</TIMEX3>TimeML
  • SGML-based language for temporal annotation.
  • Allows identification of events and times.
  • Thorough provision of links between events and times:
  • TLINK: temporal, possibly including a SIGNAL tag to a linking word
  • SLINK: subordinate
  • ALINK: aspectual
  • ISO standard.
  • TimeML - TimeBank
  • Corpus of 181 newswire texts.
  • Temporal information annotated in TimeML:
  • 6383 TLINKs,
  • 7940 EVENTs,
  • 3004kB in size.
  • Tiny compared to some other types of corpus.
  • Involved a large human annotator effort and a few different versions.
  • Biggest temporally annotated corpus.
  • TCNL
  • Developed at CMU with L. Levin.
  • Useful for reasoning between events.
  • Captures intensional meanings of expressions.
  • “Yesterday” becomes {now-|1day|} instead of something like 20090506
  • A set of operators are used to reason between operands:
  • +/- for forward/reverse shifting
  • @ for in;
  • {|2sun| @ {may}} is “the second Sunday in May”
  • & for distribution;
  • {15hour} & [{wed}:{fri}]} is “3pm from Wednesday to Friday”
  • T-BOX
  • Reading solid SGML is inconvenient for humans; a visual representation of events may be preferable.
  • Presenting events on a timeline may lead to unintentional over-specification.
  • Suggests a distance.
  • Many intervals are left with one end open
  • Plotting parts of a sentence in temporal order will destroy word order, making it hard to read
  • Annotating documents can be done more easily when events are grouped locally and visually connected.
  • T-BOX1 from Brandeis specifies a set of rules for rendering events and their relations.
  • 1: Verhagen – “Drawing TimeML relations with T-BOX” (2007)T-BOX
  • Relations only exist between nodes that are directly connected or contained.
  • This suggests: - X contains Y - Y is before Z
  • Drawing a temporal closure could provide a very cluttered and messy graph.
  • A set of guidelines are provided for reducing graphs to something more visually appealing.
  • Equivalence classes for some events.
  • Break cycles in graphs.
  • Remove derivable relations.
  • Evaluating annotations
  • Typical annotation evaluation.
  • Graph-based evaluation.
  • Evaluating annotations
  • Annotations can be compared in different ways.
  • When evaluating automated TIMEX or relation identification against a gold standard, we can measure precision and recall.
  • TimeBank is often used as a gold standard for training and evaluation or systems working in TimeML.
  • Evaluating TIMEX normalisation needs a different measure, as there are varying degrees of correctness available.
  • Graph based evaluation
  • Based on the use of minimal temporal graphs.
  • Graphs between events (intervals) are converted into graphs between points:
  • Smaller set of relations, needing only = and <
  • Simpler algebra
  • Simultaneous points are grouped into nodes.
  • Graphs over the same set of points can then be compared, based on the number of node splits and merges needed to reach one from the other.
  • Summary
  • Background and models useful for temporal information extraction.
  • Technical approaches to temporal IE.
  • How to reason about events.
  • Temporal closure & minimal annotations.
  • Notations for temporal information.
  • Evaluating temporal graphs & annotations.
  • Questions
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