KDF2020: The AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services Hilton New York Midtown New York, NY, United States, February 7-8, 2020 |
Submission link | https://easychair.org/conferences/?conf=kdf2020 |
Submission deadline | November 20, 2019 |
Author notification | December 4, 2019 |
Early registration deadline | December 13, 2019 |
Late registration deadline | January 10, 2020 |
Knowledge discovery from various data sources has gained the attention of many practitioners over the past decades. Its capabilities have expanded from processing structured data (e.g. DB transactions) to unstructured data (e.g. text images, and videos). In spite of major research focusing on extraction from news, web, and social media data, its application to data in professional settings such as legal documents, financial filings, and government reports, still present huge challenges. One reason is that the precision and recall requirements for extracted knowledge to be used in business process are fastidious.
In the financial services industry in particular, a large amount of financial analysts’ work requires knowledge extraction from different data sources, such as SEC filings, loan documents, industry reports etc., before the analysts can conduct any analysis. This manual extraction process is usually low in efficiency, error prone, and inconsistent. It is one of the key bottlenecks for financial services companies in improving their operating productivity. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. The automated processing of unstructured data to discover knowledge from complex financial documents requires a series of techniques such as linguistic processing, semantic analysis, and knowledge representation and reasoning. The design and implementation of these AI techniques to meet financial business operations requires the joint effort between academia researchers and industry practitioners.
Furthermore, alternative data like social media feeds and news are gaining traction as promising knowledge sources for financial institutions as they provide additional perspectives to the financial analysts when they make investment decisions. However, the volume of alternative data is usually vast and the valuable knowledge is always comingled with noise.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Original, unpublished full-length (10 pages)
- Short (4 pages) research papers that are not previously published, accepted to be published, or being considered for publication in any other forum
- Abstracts (1 page)
Papers should describe interesting ongoing work, new discoveries and insights, or summaries of significant projects, and be material that is suited to poster or oral presentation.
List of Topics
We invite submissions of original contributions on methods, theory, applications, and systems on artificial intelligence, machine learning, natural language processing, big data, statistical learning, data analytics, and deep learning, with a focus on knowledge extraction in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:
- Natural language processing, understanding and generation from financial documents;
- Search and question answering systems designed for financial corpora;
- Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents;
- Knowledge alignment and integration from heterogeneous data;
- AI assisted data tagging and labeling;
- Data acquisition, augmentation, feature engineering, and analysis for investment and risk management;
- Automatic data extraction from financial fillings and quality verification;
- Event discovery from alternative data and impact on organization equity price;
- AI systems for relationship extraction and risk assessment from legal documents.
Although textual data is prevalent in a large amount of finance-related business problems, we also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media etc.
Committees
Program Committee
- Zhiqiang Ma
- Mingyang Di
- Grace Bang
Organizing committee
- Dr. Sameena Shah - Sameena is a Managing Director and the Head of Data Science for S&P Global Ratings. Previously Sameena led AI strategy, Research and development for Financial and News verticals of Thomson Reuters, where Sameena and her team built and deployed several AI systems including Reuters Tracer, and Magnet. Sameena has worked or consulted for several hedge funds, Asset management firms and startups. She has won several best paper and industry awards, and holds a PhD and Masters in Machine Learning and Optimization from IIT Delhi. She served as an organizer for NIPS 2018 FEAP-AI4F workshop and invited speaker in KDD ADF 2019 workshop.
- Dr. Manuela M. Veloso - Manuela is on leave from Carnegie Mellon University (CMU) where she is Herbert A. Simon University Professor in the School of Computer Science, and where she was the Head of the Machine Learning Department until June 2018. Manuela recently joined J.P.Morgan Chase to create and head an Artificial Intelligence (AI) Research Center. She researches in AI, Robotics, and Machine Learning. At CMU, she founded and directs the CORAL research laboratory, for the study of autonomous agents that Collaborate, Observe, Reason, Act, and Learn, www.cs.cmu.edu/~coral. Veloso is AAAI Fellow, ACM Fellow, AAAS Fellow, and IEEE Fellow, Einstein Chair Professor of the Chinese National Academy of Science, the co-founder and past President of RoboCup, and past President of AAAI. Veloso and her students research a variety of autonomous robots, including mobile service robots and soccer robots. See www.cs.cmu.edu/~mmv for further information, including publications. Manuela was the Program Chair for IJCAI-07 and Program Co-chair for AAAI-05. She also served as organizers for multiple workshops.
- Dr. Quanzhi Li – Quanzhi is a senior manager at Alibaba Group. His research interests are NLP, information retrieval, data mining, and machine learning. He has worked on many AI projects in financial domain during his tenure at Alibaba, Thomson Reuters, FactSet and Financial Times. He has published 50+ peer-reviewed papers and is the organizer of the yearly IEEE International Workshop on Big Data for Financial News and Data.
- Dr. Le Song – Le is a Sr. Director of AI at Ant Financial and Associate Professor, Computational Science and Engineering, Georgia Institute of Technology. Le is heading the machine learning group at Georgia Tech. He developed core machine learning methodology, including kernel methods, feature space embedding methods, graphical models, probabilistic and stochastic modeling, deep learning models, etc. Le received his Ph.D. in Computer Science from the University of Sydney, Australia. He worked as a postdoc fellow at Carnegie Mellon University and a research scientist at Google Research before joining Georgia Tech. He has published more than 140 peer-reviewed papers and received multiple recognitions including best paper awards in NIPS 2013 and ICML 2010. He has organized six workshops in various machine learning conferences and gave tutorial talks in KDD, ICML, and WWW.
- Dr. Xiaomo Liu - Xiaomo is a Director of Data Science at S&P Global Ratings focusing on using machine learning and natural language processing to improve credit rating practices. Prior to S&P, Xiaomo was a senior research scientist at Thomson Reuters, where he invented AI algorithms and systems to help news and legal professionals to automate their workflow. Xiaomo holds a PhD in computer science from Virginia Tech and published over 30 papers and 3 patents.
Venue
The conference will be held in Hilton New York Midtown, New York, New York, USA.
Contact
All questions about submissions should be emailed to:
- xiaomo.liu@spglobal.com
- grace.bang@spglobal.com
- zhiqiang.ma@spglobal.com
- mingyang.di@spglobal.com