Temporal Processing of News: Annotation of Temporal Expressions, Verbal Events and Temporal Relations
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AbstractThe ability to capture the temporal dimension of a natural language text is essential to many natural language processing applications, such as Question Answering, Automatic Summarisation, and Information Retrieval. Temporal processing is a ¯eld of Computational Linguistics which aims to access this dimension and derive a precise temporal representation of a natural language text by extracting time expressions, events and temporal relations, and then representing them according to a chosen knowledge framework. This thesis focuses on the investigation and understanding of the di®erent ways time is expressed in natural language, on the implementation of a temporal processing system in accordance with the results of this investigation, on the evaluation of the system, and on the extensive analysis of the errors and challenges that appear during system development. The ultimate goal of this research is to develop the ability to automatically annotate temporal expressions, verbal events and temporal relations in a natural language text. Temporal expression annotation involves two stages: temporal expression identi¯cation concerned with determining the textual extent of a temporal expression, and temporal expression normalisation which ¯nds the value that the temporal expression designates and represents it using an annotation standard. The research presented in this thesis approaches these tasks with a knowledge-based methodology that tackles temporal expressions according to their semantic classi¯cation. Several knowledge sources and normalisation models are experimented with to allow an analysis of their impact on system performance. The annotation of events expressed using either ¯nite or non-¯nite verbs is addressed with a method that overcomes the drawback of existing methods v which associate an event with the class that is most frequently assigned to it in a corpus and are limited in coverage by the small number of events present in the corpus. This limitation is overcome in this research by annotating each WordNet verb with an event class that best characterises that verb. This thesis also describes an original methodology for the identi¯cation of temporal relations that hold among events and temporal expressions. The method relies on sentence-level syntactic trees and a propagation of temporal relations between syntactic constituents, by analysing syntactic and lexical properties of the constituents and of the relations between them. The detailed evaluation and error analysis of the methods proposed for solving di®erent temporal processing tasks form an important part of this research. Various corpora widely used by researchers studying di®erent temporal phenomena are employed in the evaluation, thus enabling comparison with state of the art in the ¯eld. The detailed error analysis targeting each temporal processing task helps identify not only problems of the implemented methods, but also reliability problems of the annotated resources, and encourages potential reexaminations of some temporal processing tasks.
PublisherUniversity of Wolverhampton
TypeThesis or dissertation
DescriptionA thesis submitted by Georgiana Marşic.
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The loss of short-term visual representations over time: decay or temporal distinctiveness?Mercer, Tom (American Psychological Association, 2014-12)There has been much recent interest in the loss of visual short-term memories over the passage of time. According to decay theory, visual representations are gradually forgotten as time passes, reflecting a slow and steady distortion of the memory trace. However, this is controversial and decay effects can be explained in other ways. The present experiment aimed to reexamine the maintenance and loss of visual information over the short term. Decay and temporal distinctiveness models were tested using a delayed discrimination task, in which participants compared complex and novel objects over unfilled retention intervals of variable length. Experiment 1 found no significant change in the accuracy of visual memory from 2 to 6 s, but the gap separating trials reliably influenced task performance. Experiment 2 found evidence for information loss at a 10-s retention interval, but temporally separating trials restored the fidelity of visual memory, possibly because temporally isolated representations are distinct from older memory traces. In conclusion, visual representations lose accuracy at some point after 6 s, but only within temporally crowded contexts. These findings highlight the importance of temporal distinctiveness within visual short-term memory.