Article Structure
Abstract
We present an approach for detecting salient (important) dates in texts in order to automatically build event timelines from a search query (e. g. the name of an event or person, etc.).
Introduction
Our aim here was to build thematic timelines for a general domain topic defined by a user query.
Related Work
The ISO-TimeML language (Pustejovsky et al., 2010) is a specification language for manual annotation of temporal information in texts, but, to the best of our knowledge, it has not yet actually been used in information retrieval systems.
Resources and System Overview
3.1 AFP Corpus
Temporal and Linguistic Processing
In this section, we describe the linguistic and temporal information extracted during the preprocessing phase and how the extraction is carried out.
Experiments and Results
In Section 5 .l, we propose two baseline approaches in order to give a good idea of the difficulty of the task (Section 5.4 also discusses this point).
Conclusion and Future Work
This article presents a task of “date extraction” and shows the importance of taking temporal information into consideration and how with relatively simple temporal processing, we were able to indirectly point to important events using the temporal information associated with these events.
Topics
Named Entity
- (Swan and Allen, 2000) present an approach to generating graphical timelines that involves extracting clusters of noun phrases and named entities .Page 2, “Related Work”
- It also performs named entity recog-Page 3, “Temporal and Linguistic Processing”
- nition (NER) of the most usual named entity categories and recognizes temporal expressions.Page 4, “Temporal and Linguistic Processing”
- 4.2 Named Entity RecognitionPage 4, “Temporal and Linguistic Processing”
- Named Entity (NE) Recognition is one of the outputs provided by XIP.Page 4, “Temporal and Linguistic Processing”
- Other features: 1) Lucene’s best ranking of the date 2) Number of times where the date is absolute in the text 3) Number of times where the date is relative (but normalized) in the text 4) Total number of keywords of the query in the title, sentence and named entities of retrieved documents 5) Number of times where the date modifies a reported speech verb or is extracted from reported speech.Page 7, “Experiments and Results”
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news articles
- Important events are those reported in a large number of news articles and each event is constructed according to one single query and represented by a set of sentences.Page 2, “Related Work”
- In news articles , this is the DCT.Page 4, “Temporal and Linguistic Processing”
- Figure 4 shows an example of an analyzed excerpt of a news article .Page 5, “Temporal and Linguistic Processing”
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