The annotation of temporal information in natural language sentences

Katz, Graham; Arosio, Fabrizio

doi:10.3115/1118238.1118252

Cited by 24 publications

(25 citation statements)

References 10 publications

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“…The first approach is the one made by Katz and Arosio [7], that describes a method to annotate intrasentential temporal relations, that means, if in one sentence there is more than one event, the relationship between these events will be established. A temporal interval is associated with every event in the sentence and the relation between these intervals is the final aim.…”

Section: Relationships Between Temporal Informationmentioning

confidence: 99%

“…Several proposals for the annotation of TEs have arisen in the last few years Wilson et al [15], Katz and Arosio [7], TIMEML [4], etc. since some research 'some days later' >>>>FechaA 'some days before' <<<<FechaA Table 1 Sample of some of the entries in the dictionary institutions have started to work on different aspects of temporal information.…”

Section: Tagging Of Temporal Expressionsmentioning

confidence: 99%

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Event Ordering Using TERSEO System

Saquete

Muñoz

Martínez-Barco

2004

Natural Language Processing and Information Systems

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In this paper, a method of event ordering based on temporal information resolution is presented. This method consists of two main steps: on the one hand, the recognition and resolution of the temporal expressions that can be transformed on a date, and therefore these dates establish an order between the events that contain them. On the other hand, the detection of temporal signals, for example after, that can not be transformed on a concrete date but relate two events in a chronological way. This event ordering method can be applied to Natural Language Processing systems, for example: Summarization, Question Answering, etc. It is important to emphasize that the event ordering method is based on a multilingual temporal information resolution system. Moreover, this multilinguality has been automatically obtained from a monolingual system (Spanish). The evaluation of the multilingual system is also shown in this paper, achieving a precision of 88% for Spanish and 77% for English.

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Section: Relationships Between Temporal Informationmentioning

confidence: 99%

Section: Tagging Of Temporal Expressionsmentioning

confidence: 99%

Event Ordering Using TERSEO System

Saquete

Muñoz

Martínez-Barco

2004

Natural Language Processing and Information Systems

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“…On the other hand, machine learning approaches (Katz and Arosio, 2001) can be extended with little human intervention through the use of language corpora. However, the large annotated corpora necessary to obtain high performance are not always available.…”

Section: Introductionmentioning

confidence: 99%

Multilingual Extension of Temporal Expression Recognition Using Parallel Corpora

Puchol-Blasco

Saquete

Martínez-Barco

2007

14th International Symposium on Temporal Representation and Reasoning (TIME'07)

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This paper presents the automatic extension of TERSEO to other languages, a knowledge-based system for the recognition and normalization of temporal expressions, originally developed for Spanish.TERSEO was extended to English and Italian through the automatic translation of the temporal expressions, and it was presented in previous works (see Saquete et al. (2004a)), but a new methodology has been designed with the purpose of obtaining better results in this issue.This new methodology is based on the use of parallel corpora for extending the TERSEO temporal model to other languages. In this case, two different methods have been tested: (1) automatic translation of TERSEO patterns to other languages and (2) automatic corpora annotation in the target side of parallel corpora. The main idea is focused on annotating the Spanish side of a parallel corpora, projecting the analysis to the second language, and then obtaining new TERSEO patterns (1) and new annotated corpus (2).The set of new patterns will be used to improve the current TERSEO language independent modules. Whereas the new annotated corpus will be used to train a ML system. This system will annotate new temporal expressions in the new language.

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“…Their work reports an inter-annotator reliability F-measure of 0.79 and 0.86 respectively for English corpora. (Katz, et al, 2001) describes a simple and general technique for the annotation of temporal relation information based on binary interval relation types: precedence and inclusion. Their annotation scheme could benefit a range of NLP applications and is easy to carry out.…”

Section: Related Workmentioning

confidence: 99%

How and where do people fail with time

Yang

Abney

2006

Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006 - LAC '06

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This work reports on three human tense annotation experiments for Chinese verbs in Chinese-to-English translation scenarios. The results show that inter-annotator agreement increases as the context of the verb under the annotation becomes increasingly specified, i.e. as the context moves from the situation in which the target English sentence is unknown to the situation in which the target lexicon and target syntactic structure are fully specified. The annotation scheme with a fully specified syntax and lexicon in the target English sentence yields a satisfactorily high agreement rate. The annotation results were then analyzed via an ANOVA analysis, a logistic regression model and a log-linear model. The analyses reveal that while both the overt and the latent linguistic factors seem to significantly affect annotation agreement under different scenarios, the latent features are the real driving factors of tense annotation disagreement among multiple annotators. The analyses also find the verb telicity feature, aspect marker presence and syntactic embedding structure to be strongly associated with tense, suggesting their utility in the automatic tense classification task.

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The annotation of temporal information in natural language sentences

Cited by 24 publications

References 10 publications

Event Ordering Using TERSEO System

Event Ordering Using TERSEO System

Multilingual Extension of Temporal Expression Recognition Using Parallel Corpora

How and where do people fail with time

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