Where Can we Accomplish our To-Do?: Estimating the Target Location by Analyzing the Task

Suzumura, Reiji; Matsuno, Shogo; Ohyama, Minoru

doi:10.1109/aina.2018.00074

Cited by 4 publications

(1 citation statement)

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“…The author of [23] uses indoor positioning and movement state to identify resolution place. Along similar lines, the work in [22] expects a To-do list for resolution place. They build a classifier using To-do as features and resolution place as class labels.…”

Section: Related Workmentioning

confidence: 90%

An Intelligent Recommendation-cum-Reminder System

Saxena¹,

Chaudhary²,

Maurya³

et al. 2021

Preprint

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Intelligent recommendation and reminder systems are the need of the fast-pacing life. Current intelligent systems such as Siri, Google Assistant, Microsoft Cortona, etc., have limited capability. For example, if you want to wake up at 6 am because you have an upcoming trip, you have to set the alarm manually. Besides, these systems do not recommend or remind what else to carry, such as carrying an umbrella during a likely rain. The present work proposes a system that takes an email as input and returns a recommendation-cumreminder list. As a first step, we parse the emails, recognize the entities using named entity recognition (NER). In the second step, information retrieval over the web is done to identify nearby places, climatic conditions, etc. Imperative sentences from the reviews of all places are extracted and passed to the object extraction module. The main challenge lies in extracting the objects (items) of interest from the review. To solve it, a modified Machine Reading Comprehension-NER (MRC-NER) model is trained to tag objects of interest by formulating annotation rules as a query. The objects so found are recommended to the user one day in advance. The final reminder list of objects is pruned by our proposed model for tracking objects kept during the "packing activity. " Eventually, when the user leaves for the event/trip, an alert is sent containing the reminding list items. Our approach achieves superior performance compared to several baselines by as much as 30% on recall and 10% on precision.

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Section: Related Workmentioning

confidence: 90%