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ECONLP 2019 – 2nd Workshop on Economics and Natural Language
Processing
After launching the Workshop on Economics and Natural Language
Processing (ECONLP) @ ACL 2018 in Melbourne, Australia
(
http://aclweb.org/anthology/W18-31), the 2nd edition of ECONLP
will be held on November 3 or 4 in Hong Kong at EMNLP-IJCNLP 2019
(
https://www.emnlp-ijcnlp2019.org/).
This workshop addresses the increasing relevance of natural
language processing (NLP) for regional, national and international
economy, both in terms of already operational language technology
products and systems, as well as newly emerging methodologies and
techniques reflecting the requirements at the intersection of
economics and NLP. The focus of the workshop will be on the many
ways, how NLP influences business relations and procedures,
economic transactions, and the roles of human and computational
actors involved in commercial activities.
Workshop Organizers:
* Udo Hahn Friedrich-Schiller-Universität Jena, Germany
udo.hahn@uni-jena.de<mailto:udo.hahn@uni-jena.de>
* Véronique Hoste Ghent University, Belgium
veronique.hoste@ugent.be<mailto:veronique.hoste@ugent.be>
* Zhu (Drew) Zhang Iowa State University, USA
zhuzhang@iastate.edu<mailto:zhuzhang@iastate.edu>
Important Dates:
* Submission Deadline: Monday, August 19 (aoe)
* Acceptance Notification: Monday, September 16
* Camera-Ready: Monday, September 30
* Workshop day: November 3 or 4 @ EMNLP-IJCNLP 2019
Submission information:
We invite two types of original and unpublished works: Long papers
(8 pages) should describe solid results with strong experimental,
empirical or theoretical/formal backing, short papers (4 pages)
should describe work in progress where preliminary results have
already been worked out. Accepted papers will appear in the
workshop proceedings. All papers are allowed unlimited but
sensible pages for references. Final camera-ready versions will be
allowed an additional page of content to address reviewers’
comments. All submissions must be anonymized, in PDF format (using
the EMNLP 2019 style sheets for the main conference; see
https://www.emnlp-ijcnlp2019.org/calls/papers) and must be made
through the Softconf website set up for this workshop (will be
opened soon).
Double Submission Policy: Papers that have been or will be
submitted to other meetings or publication sites must indicate
this information at submission time. However, we prohibit dual
submissions among EMNLP-IJCNLP 2019 workshops. This rule does not
necessarily prohibit an EMNLP-IJCNLP 2019 workshop from accepting
a presentation that is presented elsewhere (if the workshop has
the policy to allow that). Authors of a paper accepted for
presentation must notify the workshop organizers by the
camera-ready deadline as to whether the paper will be presented or
withdrawn.
Call for Papers
Papers submitted to this workshop should address (not excluding
other topic areas of relevance for the workshop theme):
* NLP-based (stock) market analytics, e.g., prediction of economic
performance indicators (trend prediction, performance forecasting,
etc.), by analyzing verbal statements of enterprises, businesses,
companies, and associated legal or administrative actors
* NLP-based product analytics, e.g., based on social and mass
media monitoring, summarizing reviews, classifying and mining
complaint messages and other (non)verbal types of customer
reactions to products or services
* NLP-based customer analytics, e.g., client profiling, tracking
product/company preferences, screening customer reviews or
complaints, identifying high-influentials in economy-related
communication networks
* NLP-based organization/enterprise analytics (e.g., tracing and
altering social images of organizational actors, risk prediction,
fraud analysis, predictive analysis of annual business,
sustainability and auditing reports)
* Market sentiments and emotions as evident from consumers’ and
enterprises’ verbal behavior and their communication strategies
about products and services
* Competitive intelligence services based on NLP tooling
* Relationship and interaction between quantitative (structured)
economic data (e.g., contained sales databases and associated time
series data) and qualitative (unstructured verbal) economic data
(press releases, newswire streams, social media contents, etc.)
* Information management based on the content-based organization,
packaging and archiving of verbal communication streams of
organizations and enterprises (emails, meeting minutes, business
letters, internal reporting, etc.)
* Credibility and trust models for business agents involved in the
economic process (e.g., as traders, sellers, advertisers)
extracted from text/opinion mining their current communication as
well as historic legacy data
* Deceptive or fake information recognition related to economic
objects (such as products, advertisements, etc.) or economic
actors (such as industries, companies, etc.), including opinion
spam targeting or emanating from economic actors and processes
* Verbally fluent software agents (chat bots for sales and
marketing) as reliable actors in economic processes serving
business interests, e.g., embodying models of persuasion,
information biases, fair trading
* Enterprise search engines (e-commerce, e-marketing)
* Consumer search engines, market monitors, product/service
recommender systems
* Client-supplier interaction platforms (e.g., portals, helps
desks, newsgroups) and transaction support systems based on
written or spoken natural language communication
* Multi-media and multi-modality interaction platforms, including
written/spoken language channels, supporting economic processes
* Specialized modes of information extraction and text mining in
economic domains, e.g., temporal event or transaction mining
* Information aggregation from single sources (e.g., review
summaries, automatic threading)
* Text generation in economic domains, e.g., review generation,
complaint response generation
* Ontologies for economics and adaptation of general-domain
lexicons for economic NLP
* Corpora and annotations policies (guidelines, metadata schemata,
etc.) for economic NLP
* Economy-specific text genres (business reports, sustainability
reports, auditing documents, product reviews, economic newswire,
business letters, law documents, etc.) and their usage for NLP
* Dedicated software resources for economic NLP (e.g., NER
taggers, sublanguage parsers, pipelines for processing economic
discourse)
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