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Hal-Diderot
Conference object . 2016
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https://doi.org/10.48550/arxiv...
Article . 2016
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets

Authors: Grefenstette, Gregory; Muchemi, Lawrence;

On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets

Abstract

International audience; Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.

Country
France
Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.5: Search process, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.7: Digital Libraries, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Human-Computer Interaction (cs.HC), ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval, Computer Science - Computers and Society, Semantic Analysis, Computers and Society (cs.CY), Cognitive activities, Lifelog, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.4: Retrieval models, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and Indexing, Computation and Language (cs.CL), ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.7: Digital Libraries/H.3.7.0: Collection, Taxonomy Induction

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Dumais, S., Cutrell, E., Cadiz, J., Jancke, G., Sarin, R., & Robbins, D. (2003). “Stuff I've Seen: A System for Personal Information Retrieval and Re-Use. SIGIR '03”: Proceedings of the 26th annual international ACM SIGIR conference on Research and Dev. in information retrieval. 72-79. New York, NY, USA.

Gemmell, J., Bell, G., & Lueder, R. (2004). “MyLifeBits: a personal database for everything.” Communications of the ACM (CACM) , 49 (1), 88-95.

Grefenstette, G. (2015). “Simple Hypernym Extraction Methods.” HAL-INRIASAC. Palaiseau, France

Grefenstette, G., & Muchemi, L. (2015). “Extracting Hierarchical Topic Models from the Web for Improving Digital Archive Access. Expert Workshop on Topic Models and Corpus Analysis-DARIAH Text & Data Analytics Working Group (TDAWG). Dublin-Ireland. [OpenAIRE]

Grefenstette, G., & Muchemi, L. (2016). Automatic Taxonomy Construction using a Domain Crawler and Sentence Level Phrase Co-occurrence Heuristics.

Guralnick, D. (2007). The Importance of the Learner's Environmental Context in the Design of M-Learning Products. 2nd International Conference on “Interactive Mobile and Computer aided Learning”. Amman, Jordan.

Gurrin, C., Smeaton, A., & Aiden, D., (2014). LifeLogging: Personal Big Data, Foundations and Trends in Information Retrieval. Vol. 8, No. 1, 1-107

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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