TY  - JOUR
A1  - Puri, Manish
A1  - Varde, Aparna S.
A1  - Melo, Gerard de
T1  - Commonsense based text mining on urban policy
T2  - Language resources and evaluation
N2  - Local laws on urban policy, i.e., ordinances directly affect our daily life in various ways (health, business etc.), yet in practice, for many citizens they remain impervious and complex. This article focuses on an approach to make urban policy more accessible and comprehensible to the general public and to government officials, while also addressing pertinent social media postings. Due to the intricacies of the natural language, ranging from complex legalese in ordinances to informal lingo in tweets, it is practical to harness human judgment here. To this end, we mine ordinances and tweets via reasoning based on commonsense knowledge so as to better account for pragmatics and semantics in the text. Ours is pioneering work in ordinance mining, and thus there is no prior labeled training data available for learning. This gap is filled by commonsense knowledge, a prudent choice in situations involving a lack of adequate training data. The ordinance mining can be beneficial to the public in fathoming policies and to officials in assessing policy effectiveness based on public reactions. This work contributes to smart governance, leveraging transparency in governing processes via public involvement. We focus significantly on ordinances contributing to smart cities, hence an important goal is to assess how well an urban region heads towards a smart city as per its policies mapping with smart city characteristics, and the corresponding public satisfaction.
KW  - Commonsense reasoning
KW  - Opinion mining
KW  - Ordinances
KW  - Smart cities
KW  - Social
KW  - media
KW  - Text mining
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63575
SN  - 1574-020X
SN  - 1574-0218
VL  - 57
SP  - 733
EP  - 763
PB  - Springer
CY  - Dordrecht [u.a.]
ER  -