Reviews on Temporal Information Retrieval

doi:10.11925/infotech.2096-3467.2017.01.02

Data Analysis and Knowledge Discovery

2017, Vol. 1

Issue (1): 3-15 DOI: 10.11925/infotech.2096-3467.2017.01.02

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Reviews on Temporal Information Retrieval

Zhang Xiaojuan(

), Han Yi

School of Computer and Information Science, Southwest University, Chongqing 400715, China

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Abstract

[Objective]This study aims to summarize the research status of temporal information retrieval (T-IR) and to provide theoretical basis for the study of the relevant scholars to better grasp the T-IR problems. [Coverage] We first used Google Scholar to search related literatures by typing the keywords “termporal information retireval” in Chinese and English repectively, without time limit. After getting some related literatures, we further used the retrospective method to get more related literatures. Finally, we get 92 literatures totally. [Methods] Based on method of literature survey and methods of inducting and summarizing, a survey of the existing literature on temporal information retrieval was presented from the following three aspects: extracting temporal information from document, identifying temporal information in queries and temporal ranking model. [Results] The problems and challenges existing in temporal information retrieval are as follows: little related work existing in China while most of related work existing in foreign countries; lack of methods of data collection and data indexing reflecting dynamic characteristics of real network; ignorance of the important role of the entity and event represent time information when identify the focus time of document; lack of the predicting intent for non-periodic queries and the improvement of reproducibility of temporal information retrieval model experiment to be needed. [Limitations] This paper did not review the document crawling, document index and corresponding application of temporal information retrieval. [Conclusions] The construction of standardized evaluation datasets and non-parameter temporal information retrieval models will be the future research trends of T-IR.

Key words： Temporal Information Retrieval Temporal Information Temporal Intent Temporal Ranking

Received: 15 August 2016 Published: 22 February 2017

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Cite this article:

Zhang Xiaojuan,Han Yi. Reviews on Temporal Information Retrieval. Data Analysis and Knowledge Discovery, 2017, 1(1): 3-15.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.01.02 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I1/3

相关会议名称	会议主要任务	数据集内容		数据集时间跨度	实验结果评价指标
SemEval 2015时间与空间任务(SemEval 2015 - Time and Space Track)^①(①http://alt.qcri.org/semeval2015/index.php?id=tasks.)	与实体相关事件的识别; 时态性问答; 时态临床信息抽取; 空间信息识别等	新闻、论文、维基百科、博客与临床数据集		1960年-2014年	F1值(F1-score)、召回率(Recall)与准确率(Precision)
TREC 时态摘要任务(TREC Temporal Summarization Track)^②(②http://trec.nist.gov/pubs/call2016.html.)	提取某事件相关的实时性摘要信息	TREC 知识库扩展数据集(TREC KBA Stream Corpus): 来自于新闻或者其他社交媒体中带有时间戳的文档		2011年10月- 2013年2月中旬	(归一化)期望获益指标(nEG(S))、全面性指标(Comprehensiveness Metric, C(S))、期望延迟指标(Excepted Latency Metric, E[latency])及综合以上三类评测指标的归一化期望延迟获益的调和平均值指标(Harmonic Mean of normalized EL, EG_τ(S))与延迟全面性性指标(Latency Comprehensiveness, C_τ (S))
TERC 知识资源扩展任务(TRCE Knowledge Base Acceleration Track: KBA)^③(③http://trec-kba.org/.)	通过时态排序筛选出与预定义实体相关的文档, 并以此来扩展知识资源(如Wikipedia)	TREC 知识库扩展数据集(TREC KBA Stream Corpus)		2011年10月- 2013年2月中旬	F_1准确度指标(F_1 Accuracy)与Scaled Utility指标
相关会议名称	会议主要任务	数据集内容	数据集时间跨度		实验结果评价指标
NTCIR时态信息获取任务(NTCIR Temporal Information Access Temporalia )^①(①https://sites.google.com/site/ntcirtemporalia/.)	时态意图消歧(Temporal Intent Disambiguation: TID); 时态信息检索(Temporal Information Retrieval, TIR) 时态多样化检索(Temporally Diversified Retrieval: TDR)	英文数据集: 由LivingKnowledge 项目创建的“LivingKnowledge 新闻和博客标注子数据集”; 中文数据集: Sogou 全网新闻数据集(SogouCA)与Sogou 互联网语料库(SogouT)	英文数据集: 2011年 5月-2013年3月; 中文数据集: SogouCA, 2012年6月-2013年 7月; SogouT, 2008年 11月		TID子任务的评测指标: 平均每类别的绝对损失(Averaged Per-class Absolute Lose)与平均余弦相似度(Averaged Cosine Similarity); TIR 子任务的评测指标: P@20、 nDCG@20与Q@20指标; TDR子任务的评测指标: a-nDCG与D#nDCG指标
TREC微博任务中Tweet 时间表生成任务(Tweet TimeLine Generation Task of the TREC Microblog Track: TTG)^②(②https://github.com/lintool/twitter-tools/wiki/TREC-2015-Track-Guidelines.)	返回在时间点t之前与查询Q相关Tweet 的摘要信息	TREC 微博数据集 (TREC Microblog Dataset)	2014年		聚类准确率(Cluster Precision))、加权聚类召回率(Weighted Cluster Recall)与非加权聚类召回率(Unweighted Cluster Recall )

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