2021 EDITION

We invite two types of original and unpublished works: Long papers (8 pages) should describe solid results with strong experimental, empirical or theoretical/formal backing, short papers (4 pages) should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 an unlimited but sensible number of 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 2021 style sheets for the main conference; see https://2021.emnlp.org/call-for-papers/style-and-formatting) and must be made through the Softconf website set up for this workshop (https://www.softconf.com/emnlp2021/ECONLP/).

Papers submitted to this workshop should address:

• 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 pro-actively altering social images of organizational actors, risk prediction, fraud analysis, predictive analysis of annual business, sustainability and auditing reports)
• NLP-based analysis of macro-economic phenomena in which national economies and the (inter)national banking system (IMF, Fed, PBoC, ECB) play an influential role
• 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 communication behavior (including historic legacy data)
• Deception 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 (chatbots for sales and marketing) as virtual 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 and knowledge graphs 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, etc.) and their usage for NLP (e.g., classification, filtering, etc.)
• Dedicated software resources for economic NLP (e.g., NER taggers, sublanguage parsers, pipelines for processing verbale data from economic discourse)