scholarly journals Natural Language Use as Bipartite Networks in Psychology

2021 ◽  
Author(s):  
Juan C. Correa
Keyword(s):  
Natural Language ◽  
Network Theory ◽  
Language Use ◽  
Future Research ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Research Directions ◽  
Future Research Directions ◽  
Data Source ◽  
Opinion Article

Natural language as a data source is quite common in different divisions of psychology. Among the several ways to analyze the information conveyed by natural language, psychologists rarely use bipartite networks despite the strong potential that this network perspective has for enriching psychology's research toolbox. This opinion article aims to provide a viewpoint on current advances and promising future research directions on modeling natural language as a bipartite network structure, using word-of-mouth as the basis for a tutorial exposition that paves the way for others to leverage the opportunities provided by network theory.

scholarly journals Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

2021 ◽  
Vol 2 ◽  
pp. 1-21
Author(s):  
Gengchen Mai ◽  
Krzysztof Janowicz ◽  
Rui Zhu ◽  
Ling Cai ◽  
Ni Lao
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Question Answering ◽  
Future Research ◽  
Open Domain ◽  
Future Directions ◽  
Research Directions ◽  
Classification Framework ◽  
Substantial Progress ◽  
Future Research Directions

Abstract. As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

Data Mining

2012 ◽  
pp. 75-94 ◽  
Author(s):  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Pattern Mining ◽  
Data Sources ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

Data Mining provides approaches for the identification and discovery of non-trivial patterns and models hidden in large collections of data. In the applied natural language processing domain, data mining usually requires preprocessed data that has been extracted from textual documents. Additionally, this data is often integrated with other data sources. This chapter provides an overview on data mining focusing on approaches for pattern mining, cluster analysis, and predictive model construction. For those, we discuss exemplary techniques that are especially useful in the applied natural language processing context. Additionally, we describe how the presented data mining approaches are connected to text mining, text classification, and clustering, and discuss interesting problems and future research directions.

Flattening Relations in the Sharing Economy

2022 ◽  
pp. 26-51
Author(s):  
Alexandre Borba Da Silveira ◽  
Norberto Hoppen ◽  
Patricia Kinast De Camillis
Keyword(s):  
Network Theory ◽  
Methodological Approach ◽  
Actor Network Theory ◽  
Sharing Economy ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
Future Research Directions ◽  
Digital Agency ◽  
Behavioral Attitudes

The sharing economy (SE) includes economic, social, and technological arrangements to promote collaborative relations between users and providers willing to share assets through digital platforms (DP). Even evolving fast, there is an opportunity to discuss how DP establishes connections between users and providers and uses a digital agency to mediate and flatten consumption relations in SE. Therefore, the authors propose a framework and future research directions that explore characteristics of the actants (roles, agency, behavioral attitudes) in the process of flattening consumption relations through DP in SE (connections, mediation, induction). To structure this framework, the authors consolidated the various definitions of its main elements and adopted the actor-network theory concept of translation as the theoretical-methodological approach to analyze the associations that determined how flattening consumption relations occur in SE.

Acquisitions and applications of structure preference relations in Chinese

2000 ◽  
Vol 6 (2) ◽  
pp. 163-181 ◽  
Author(s):  
QIANG ZHOU ◽  
FUJI REN
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Preference Relation ◽  
Future Research ◽  
Research Directions ◽  
Quantitative Distribution ◽  
Future Research Directions ◽  
Automatic Acquisition ◽  
Representation Scheme

In this paper, we propose a new ambiguity representation scheme; Structure Preference Relation (SPR), which consists of useful quantitative distribution information for ambiguous structures. Two automatic acquisition algorithms, the first acquired from a treebank, and the second acquired from raw texts, are introduced, and some experimental results which prove the availability of the algorithms are also given. Finally, we introduce some SPR applications in linguistics and natural language processing, such as preference-based parsing and the discovery of representative ambiguous structures, and propose some future research directions.

scholarly journals Everything is Going Electronic, so do Services and Service Quality: Bibliometric Analysis of E-Services and E-Service Quality

2021 ◽  
Vol 15 (18) ◽  
pp. 148
Author(s):  
Javed Ali ◽  
Ahmad Jusoh ◽  
NorHalima Idris ◽  
Alhamzah F. Abbas ◽  
Ahmed H. Alsharif
Keyword(s):  
Service Quality ◽  
Bibliometric Analysis ◽  
Future Research ◽  
Research Directions ◽  
Quality Literature ◽  
Business Excellence ◽  
Future Research Directions ◽  
Data Source ◽  
Scopus Database ◽  
Number Of Publications

The purpose of the study was to explore the developments in ‘e-services and e-service quality’ from 2000 to 2020. Data Source: Scopus database was used to conduct the bibliometric analysis of 404 documents. Method: VOSviewer soft-ware was used to analyse the research articles associated with ‘e-services and e-service quality’ research. Search was limited to keywords of ‘e-services OR e-service and e-service quality’. Findings: Results revealed that the field of ‘Busi-ness, Management and Accounting’ had the highest number of publications. To-tal Quality Management and Business Excellence was found at the top among the most productive journals in chosen search. Chang W.-I. and Yuan S.-T. from Taiwan were found to be the leading authors among top ten authors. United States and National Cheng Kung University of Taiwan were found to be the lead-ing country and institution in the selected search of e-service and e-service quali-ty. Originality/ Value: This study, to best of our knowledge, is the first of its kind in mapping the ‘e-services and e-service quality’ literature in Scopus. This will aid in shaping the central theme and set the future research directions for the researchers.

scholarly journals Deep Learning for Video Captioning: A Review

2019 ◽  
Author(s):  
Shaoxiang Chen ◽  
Ting Yao ◽  
Yu-Gang Jiang
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Video Captioning ◽  
Future Research Directions ◽  
Review State ◽  
Vision And Language

Deep learning has achieved great successes in solving specific artificial intelligence problems recently. Substantial progresses are made on Computer Vision (CV) and Natural Language Processing (NLP). As a connection between the two worlds of vision and language, video captioning is the task of producing a natural-language utterance (usually a sentence) that describes the visual content of a video. The task is naturally decomposed into two sub-tasks. One is to encode a video via a thorough understanding and learn visual representation. The other is caption generation, which decodes the learned representation into a sequential sentence, word by word. In this survey, we first formulate the problem of video captioning, then review state-of-the-art methods categorized by their emphasis on vision or language, and followed by a summary of standard datasets and representative approaches. Finally, we highlight the challenges which are not yet fully understood in this task and present future research directions.

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