Assigning time-stamps to event-clauses

Filatova, Elena; Hovy, Eduard

doi:10.3115/1118238.1118250

Cited by 66 publications

(49 citation statements)

References 8 publications

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“…Mani [10] gives a good review about the recent trend. Research works in this area can be classified into four types: designing annotation scheme for temporal information representation [4,6,12]; developing temporal ontology which covers temporal objects and their relationships between each other [2,7]; Identifying time-stamps of events or temporal relationships between events [5,9]; Identifying and normalizing temporal expressions from different languages [1,3,8,11,13,15].…”

Section: Related Workmentioning

confidence: 99%

CTEMP: A Chinese Temporal Parser for Extracting and Normalizing Temporal Information

Wang

et al. 2005

Lecture Notes in Computer Science

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Abstract. Temporal information is useful in many NLP applications, such as information extraction, question answering and summarization. In this paper, we present a temporal parser for extracting and normalizing temporal expressions from Chinese texts. An integrated temporal framework is proposed, which includes basic temporal concepts and the classification of temporal expressions. The identification of temporal expressions is fulfilled by powerful chart-parsing based on grammar rules and constraint rules. We evaluated the system on a substantial corpus and obtained promising results.

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Section: Related Workmentioning

confidence: 99%

CTEMP: A Chinese Temporal Parser for Extracting and Normalizing Temporal Information

Wang

et al. 2005

Lecture Notes in Computer Science

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“…One of the systems based on time-stamping is the one by Filatova and Hovy [5] which describes a procedure for arranging into a time-line the contents of news stories describing the development of some situation. This approach describes a method for breaking news stories into their constituent events and assigning time-stamps to them.…”

Section: Time-stampingmentioning

confidence: 99%

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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“…This was done to extract dates, temporal adjuncts, various temporal markers, and to achieve a somewhat coarse clause-splitting (one finite verb in each clause) and to attach temporal adjuncts to the appropriate clause (this is of course a potentially large source of errors). Relative clauses are extracted and put at the end of their sentence of origin, in a way similar to [5].…”

Section: Stages For the Extraction Of Temporal Relationsmentioning

confidence: 99%

“…pp (5) to pqp (8), then R becomes the previous E, and E is reset to nothing), or not (all others tenses, then E and R are reset when there is a change).…”

Section: Translation: Chances Of a Breakthrough At The Aqaba Summit Dmentioning

confidence: 99%