Dataset Information

Event-Dataset: Temporal information retrieval and text classification dataset.

ABSTRACT: 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, C={Qs,Ds}, where C represents the dataset, Qs is query set Qs={q1,q2,q3,…….,q35} and for each qi there is a set of news articles qi={dr,dnr} . dr,dnr 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.

SUBMITTER: Khan SUR

PROVIDER: S-EPMC6554222 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Event-Dataset: Temporal information retrieval and text classification dataset.

Khan Shafiq Ur Rehman SUR Islam Muhammad Arshad MA

Data in brief 20190523

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 e ...[more]

PMID: 31194158

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Project description:BackgroundPatient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources.ObjectiveThe primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials.MethodsThis approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval.ResultsWe provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one.ConclusionsText from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors' questions would be addressed by the materials currently provided to them.

Project description:Memories compete for retrieval when they are related to a common retrieval cue. Previous research has shown that retrieval of a target memory may lead to subsequent retrieval-induced forgetting (RIF) of currently irrelevant competing memories. In the present study, we investigated the time course of competitive semantic retrieval and examined the neurocognitive mechanisms underlying RIF. We contrasted two theoretical accounts of RIF by examining a critical aspect of this memory phenomenon, namely the extent to which it depends on successful retrieval of the target memory. Participants first studied category-exemplar word-pairs (e.g. Fruit-Apple). Next, we recorded electrophysiological measures of brain activity while the participants performed a competitive semantic cued-recall task. In this task, the participants were provided with the studied categories but they were instructed to retrieve other unstudied exemplars (e.g. Fruit-Ma__?). We investigated the event-related potential (ERP) correlates of retrieval success by comparing ERPs from successful and failed retrieval trials. To isolate the ERP correlates of continuous retrieval attempts from the ERP correlates of retrieval success, we included an impossible retrieval condition, with incompletable word-stem cues (Drinks-Wy__) and compared it with a non-retrieval presentation baseline condition (Occupation-Dentist). The participants' memory for all the studied exemplars was tested in the final phase of the experiment. Taken together, the behavioural results suggest that RIF is independent of target retrieval. Beyond investigating the mechanisms underlying RIF, the present study also elucidates the temporal dynamics of semantic cued-recall by isolating the ERP correlates of retrieval attempt and retrieval success. The ERP results revealed that retrieval attempt is reflected in a late posterior negativity, possibly indicating construction of candidates for completing the word-stem cue and retrieval monitoring whereas retrieval success was reflected in an anterior positive slow wave.

Dataset Information

Event-Dataset: Temporal information retrieval and text classification dataset.

Publications

Event-Dataset: Temporal information retrieval and text classification dataset.

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