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The main focus of the Dagstuhl seminar 05151 was on TimeML-based temporal annotation and reasoning. We were concerned with three main points: how effectively can one use the TimeML language for consistent annotation, determining how useful such annotation is for further processing, and determining what modifications should be applied to the standard to make it more useful for applications such as question-answering and information retrieval.

TBox is a new way of visualizing the temporal relations in TimeML graphs. Until recently, TimeML’s temporal relations were presented as rows in a table or as directed labeled edges in a graph. Neither mode of representation scales up nicely when bigger documents are considered and both make it harder than necessary to get a quick picture of the temporal structure of a document. TBox uses left-to-right arrows, box-inclusions and stacking as three distinct ways to visualize precedence, inclusion and simultaneity.

Consider example (a), below. When the temporal adverbial is in sentence-final position as in (i.a), it can attach syntactically at the VP-level or at sentence-level:

i. a. Mary has worked in Amsterdam since 1992.

b. Since 1992 Mary has worked in Amsterdam.

Hitzeman (1993, 1997) argues that these different positions allow it to take on two readings: one in which there was some period between 1992 and speech time during which Mary worked in Amsterdam and another in which Mary has worked in Amsterdam for the period from 1992 until speech time. In contrast, sentence (i.b), in which the adverbial must attach at sentence-level, has only the second reading. If an initial-position adverbial unambiguously specifies the time of the event expressed by a sentence, then it should be a useful tool for a reader trying to determine the order of events in a narrative. To test the hypothesis that initial-position adverbials occur more often in texts describing events with some temporal order (i.e., a story line), I compare the use of these adverbials in narrative text and in non-narratives. The results show that significantly more initial-position adverbials are used in narratives. I then test the individual narratives and show that the significant difference in use of initial-position adverbials is correlated with the amount of flashback material in a narrative, i.e., with the complexity of the story line.

TimeML is an expressive language for temporal information, but its rich representational properties raise the bar for traditional information extraction methods when applied to the task of text-to-TimeML analysis. We analyse the extent to which TimeBank, the reference corpus for TimeML, supports development of TimeML-compliant analytics. The first release of the corpus exhibits challenging characteristics: small size and some noise. Nonetheless, a particular design of a time annotator trained on TimeBank is able to exploit the data in an implementation which deploys a hybrid analytical strategy of mixing aggressive finite-state processing over linguistic annotations with a state-of-the-art machine learning technique capable of leveraging large amounts of unannotated data. We present our design, in light of encouraging performance results; we also interpret these results in relation to a close analysis of TimeBank’s annotation ‘profile’. We conclude that even the first release of the corpus is invaluable; we further argue for more infrastructure work needed to create a larger and more robust reference corpus.

This paper presents a prototype system that extracts events from the United States Code on U.S. immigration nationality and links these events to temporal constraints, such as in . In addition, the paper provides an overview of what kinds of other temporal information can be found in different types of legal documents. In particular, it discusses how one could do further reasoning with the extracted temporal information for case law and statutes.

The CTTN–system is a computer program which provides advanced processing of temporal notions. The basic data structures of the CTTN–system are time points, crisp and fuzzy time intervals, labelled partitionings of the time line, durations, and calendar systems. The labelled partitionings are used to model periodic temporal notions, quite regular ones like years, months etc., partially regular ones like timetables, but also very irregular ones like, for example, dates of a conference series. These data structures can be used in the temporal specification language GeTS (GeoTemporal Specifications). GeTS is a functional specification and programming language with a number of built-in constructs for specifying customised temporal notions.

CTTN is implemented as a Web server and as a C++ library. This paper gives a short overview over the current state of the system and its components.

The XML-based markup language TimeML encodes temporal and event-time information for use in automatic text processing. The TimeML annotation of a text contains information about the temporal intervals that are mentioned in the text as well as the relationship of these temporal intervals to the times and events mentioned in the text. We provide here a formal denotational semantics for TimeML, addressing problems of operator scope that arise in the context of a “flat” representation language and providing a sketch of an intensional extension to the main extensional semantics.

TimeML is a specification language for the annotation of events and temporal expressions in natural language text. In addition, the language introduces three relational tags linking temporal objects and events to one another. These links impose both aspectual and temporal ordering over time objects, as well as mark up subordination contexts introduced by modality, evidentiality, and factivity. Given the richness of this specification, the TimeML working group decided not to include the arguments of events within the language specification itself. Full reasoning and inference over natural language texts clearly requires knowledge of events along with their participants. In this paper, we define the appropriate role of argumenthood within event markup and propose that TimeML should make a basic distinction between arguments that are events and those that are entities. We first review how TimeML treats event arguments in subordinating and aspectual contexts, creating event-event relations between predicate and argument. As it turns out, these constructions cover a large number of the argument types selected for by event predicates. We suggest that TimeML be enriched slightly to include causal predicates, such as , since these also involve event-event relations. As such, causal relationships will be a relation type for the new Discourse Link that will also encode other discourse relations such as elaboration. We propose that all other verbal arguments be ignored by the specification, and any predicate-argument binding of participants to an event should be performed by independent means. In fact, except for the event-denoting arguments handled by the extension to TimeML proposed here, almost full temporal ordering of the events in a text can be computed without argument identification.

Representation and reasoning about time and events is a fundamental aspect of our cognitive abilities and intrinsic to our construal of the structure of our personal and historical lives and recall of past experiences. These capabilities also underlie our understanding of narrative language. This paper describes an abstract device called a Chronoscope, that allows a temporal representation (a set of events and their temporal relations) to be viewed based on temporal abstractions. The temporal representation is augmented with abstract events called that stand for discourse segments. The temporal abstractions allow one to collapse temporal relations, or view the representation at different time granularities (hour, day, month, year, etc.), with corresponding changes in event characterization and temporal relations at those granularities. The paper situates Chronoscopes in terms of systems for automatically extracting the temporal structure of narratives.