Generic method for detecting focus time of documents

Jatowt, Adam; Yeung, Ching Man Au; Tanaka, Katsumi

doi:10.1016/j.ipm.2015.05.001

Cited by 10 publications

(15 citation statements)

References 30 publications

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“…The comparison of the proposed method with the state-of-the-art can be found in Table 3. Unfortunately, original state-of-the-art datasets [1] are not available. Therefore, test datasets are created by accessing the same web sources and following the steps described by Morbidoni and Cucchiarelli [35].…”

Section: Resultsmentioning

confidence: 99%

“…We collect paragraphs from the following web sites reporting main events related to five countries. The web sites we consider are the same as in [1]: History Orb, 6 History World, 7 BBC Timelines, 8 and Infoplease. 9 In order to reproduce a dataset as similar as possible to the dataset described in [1], we use the Wayback Machine 10 to access the snapshot of the websites recorded in January 2015, which is the date that was reported to create the web dataset.…”

Section: Resultsmentioning

confidence: 99%

“…In temporal focus estimation of documents, the proposed method relies on the local contextual association of terms with time points using PMI. Jatowt et al [1] used the contextual association of terms with time points based on the Jaccard coefficient. The proposed method's success can be attributed to the use of PMI, because in the case of PMI, a high association means that cooccurrence is bigger than random choice, while the Jaccard coefficient reports high cooccurrence without considering the divergence from randomness.…”

Section: Discussionmentioning

confidence: 99%

“…When it comes to calculation of word-time point associations, in the literature, the Jaccard coefficient [32] is used for this purpose [1]. We propose to use PMI [33] as a word association measure in calculating a document's temporal focus.…”

Section: Temporal Entropy With Pmimentioning

confidence: 99%

“…Location and time expressions can be used as clues to predict the spatiotemporal focus of unstructured text. A key approach in document focus time estimation [1] relies on the association of words with time expressions in documents and the time expression that has a distinctive association with terms in documents is selected as the target. Words' contributions to the overall document-time association score are weighted with respect to how strongly associated they are with time points.…”

Section: Introductionmentioning

confidence: 99%

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Estimating spatiotemporal focus of documents using entropy with PMI

Yaşar¹,

Tekir²

2020

Turk J Elec Eng & Comp Sci

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Many text documents are spatiotemporal in nature, i.e. contents of a document can be mapped to a specific time period or location. For example, a news article about the French Revolution can be mapped to year 1789 as time and France as place. Identifying this time period and location associated with the document can be useful for various downstream applications such as document reasoning or spatiotemporal information retrieval. In this paper, temporal entropy with pointwise mutual information (PMI) is proposed to estimate the temporal focus of a document. PMI is used to measure the association of words with time expressions. Moreover, a word's temporal entropy is considered as a weight to its association with a time point and a single time point with the highest overall score is chosen as the focus time of a document. The proposed method is generic in the sense that it can also be applied for spatial focus estimation of documents. In the case of spatial entropy with PMI, PMI is used to calculate the association between words and place entities. The effectiveness of our proposed methods for spatiotemporal focus estimation is evaluated on diverse datasets of text documents. The experimental evaluation confirms the superiority of our proposed temporal and spatial focus estimation methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Temporal Entropy With Pmimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating spatiotemporal focus of documents using entropy with PMI

Yaşar¹,

Tekir²

2020

Turk J Elec Eng & Comp Sci

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Event Categorization from News Articles Using Machine Learning Techniques

Shanmugavadivel,

Subramanian,

Vasantharan

et al. 2024

Communications in Computer and Information Science

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Event-Dataset: Temporal information retrieval and text classification dataset

Khan

Islam

2019

Data in Brief

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Recently, Temporal Information Retrieval (TIR) has grabbed the major attention of the information retrieval community. TIR exploits the temporal dynamics in the information retrieval process and harnesses both textual relevance and temporal relevance to fulfill the temporal information requirements of a user Ur Rehman Khan et al., 2018. The focus time of document is an important temporal aspect which is defined as the time to which the content of the document refers Jatowt et al., 2015; Jatowt et al., 2013; Morbidoni et al., 2018, Khan et al., 2018. To the best of our knowledge, there does not exist any standard benchmark data set (publicly available) that holds the potential to comprehensively evaluate the performance of focus time assessment strategies. Considering these aspects, we have produced the Event-dataset, which is comprised of 35 queries and set of news articles for each query. Such that, where C represents the dataset, is query set and for each there is a set of news articles . are sets of relevant documents and non-relevant documents respectively. Each query in the dataset represents a popular event. To annotate these articles into relevant and non-relevant, we have employed a user-study based evaluation method wherein a group of postgraduate students manually annotate the articles into the aforementioned categories. We believe that the generation of such dataset can provide an opportunity for the information retrieval researchers to use it as a benchmark to evaluate focus time assessment methods specifically and information retrieval methods generically.

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Generic method for detecting focus time of documents

Cited by 10 publications

References 30 publications

Estimating spatiotemporal focus of documents using entropy with PMI

Estimating spatiotemporal focus of documents using entropy with PMI

Event Categorization from News Articles Using Machine Learning Techniques

Event-Dataset: Temporal information retrieval and text classification dataset

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