Knowledge discovery from various data sources has gained the attention of many practitioners in recent 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 substantial research focusing on discovery from news, web, and social media data, its applications to datasets in professional settings such as legal documents, financial filings, and government reports, still present huge challenges. Possible reasons are that the precision and recall requirements for extracted knowledge to be used in business processes are fastidious, and signals gathered from these knowledge discovery tasks are usually very sparse and thus the generation of supervision signals is quite challenging.
In the financial services industry particularly, a large amount of financial analysts’ work requires knowledge discovery and extraction from different data sources, such as SEC filings, loan documents, industry reports, etc., before they can conduct any analysis. This manual extraction process is usually inefficient, error-prone, and inconsistent. It is one of the key bottlenecks for financial services companies to improve 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 & reasoning. The design and implementation of these AI techniques to meet financial business operations require a joint effort between academia researchers and industry practitioners.
Furthermore, based on the reflection and feedback from our 2020 and 2021 AAAI KDF workshops, the 2022 workshop is particularly interested in financial domain-specific representation learning, open financial datasets and benchmarking, and transfer learning application on financial data.
We invite submissions of original contributions on methods, theories, applications, and systems on artificial intelligence, machine learning, natural language processing & understanding, big data, statistical learning, data analytics, and deep learning, with a focus on knowledge discovery in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:
- Representation learning, distributed representations learning and
encoding in natural language processing for financial documents;
- Synthetic or genuine financial datasets and benchmarking baseline
- Transfer learning application on financial data, knowledge
distillation as a method for compression of pre-trained models or
adaptation to financial datasets;
- 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;
- Using multi-modal data in knowledge discovery for financial
- 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
- Event discovery from alternative data and impact on organization
- AI systems for relationship extraction and risk assessment from legal
- Accounting for Black-Swan events in knowledge discovery methods
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.
All submissions must be original contributions and will be peer reviewed, single-blinded. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. We accept two types of submissions - full research paper no longer than 8 pages (including references) and short/poster/position paper with 2-4 pages. Submissions will be accepted via EasyChair: https://easychair.org/conferences/?conf=kdf22.
*Workshop Organizing Committee:*
- Xiaomo Liu, J.P. Morgan Chase AI Research
- Zhiqiang Ma, J.P. Morgan Chase AI Research
- Armineh Nourbakhsh, J.P. Morgan Chase AI Research
- Sameena Shah, J.P. Morgan Chase AI Research
- Gerard de Melo, Hasso Plattner Institute
- Le Song, Mohamed bin Zayed University of Artificial Intelligence
*Workshop URL*: https://aaai-kdf.github.io/kdf2022/
- Abstract Submission (optional): *11/05/2021*
- Paper Submission Deadline: *11/12/2021*
- Notification of Acceptance: *12/06/2021*
- Workshop Date:* 2/28/2022 or 3/01/2022*
We look forward to your participation. For general inquiries about KDF, please write to inquiry.kdf2022 at easychair.org.
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