The research and progress of Natural Language Processing (NLP) in Finance Domain (FinTech).
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FinIR (SIGIR): Information Retrieval in Finance
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FNP (COLING): Financial Narrative Processing
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FinNLP(IJCAI): Financial Technology and Natural Language Processing
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KDF (AAAI): Knowledge Discovery from Unstructured Data in Financial Services
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ECONLP (EMNLP): Economics and Natural Language
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Robust AI in FS (NeurIPS)
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ADF (KDD): Anomaly Detection in Finance
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FiQA (WWW): Financial Opinion Mining and Question Answering
There are various NLP tasks that have been developed in financial domain.
Each example sentence is classifed into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the sentences are considered from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral.
Given a text instance in the financial domain (microblog message, news statement or headline) in English, detect the target aspects which are mentioned in the text (from a pre-defined list of aspect classes) and predict the sentiment score for each of the mentioned targets. Sentiment scores will be defined using continuous numeric values ranged from -1(negative) to 1(positive).
The volume of available financial information is increasing sharply and therefore the study of NLP methods that automatically summarise content has grown rapidly into a major research area.
The Financial Narrative Summarisation (FNS 2020) aims to demonstrate the value and challenges of applying automatic text summarisation to financial text written in English, usually referred to as financial narrative disclosures. The task dataset has been extracted from UK annual reports published in PDF file format.
The target of this task is to produce one summary for each annual report. The summary length should not exceed 1000 words. Both extractive and abstractive summary are acceptable.
Given a corpus of structured and unstructured text documents from different financial data sources in English (microblogs, reports, news) build a Question Answering system that answers natural language questions. For this challenge, part of the questions will be opinionated, targeting mined opinions and their respective entities, aspects, sentiment polarity and opinion holder.
The challenge takes both an Information Retrieval (IR) and a Question Answering (QA) perspective. Systems can rank relevant documents from the reference knowledge base with regard to a natural language question or generate their own answer. The document relevance is evaluated using 0 as non-relevant document score and 1, otherwise. The relevant score information is implicit if you consider the question-doc matches information contained in the training FiQA question-doc data source.
Financial analysis needs factual data, but also explanation on the variability of these data. Data state facts, but provide little to no knowledge regarding how these facts materialised. The Financial Document Causality Detection Task aims to develop an ability to explain, from external sources, the reasons why a transformation occurs in the financial landscape, as a preamble to generating accurate and meaningful financial narrative summaries. Its goal is to evaluate which events or which chain of events can cause a financial object to be modified or an event to occur, regarding a given external context. This context is available in the financial news, but due to the high volatility of such information, mapping an external cause to a given consequence is not trivial.
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Event extraction
This is the only manually annotated dataset for event detection in news articles.
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Relation Extraction
TBD
Sentences are basic units of the written language and detecting the beginning and end of sentences, or sentence boundary detection (SBD) is a foundational first step in many Natural Language Processing (NLP) applications, such as POS tagging; syntactic, semantic, and discourse parsing; information extraction; or machine translation.
This task focuses on extracting well segmented sentences from Financial prospectuses by detecting their beginning and ending boundaries. These are official PDF documents in which investment funds precisely describe their characteristics and investment modalities. The most important step of extracting any information from these files is to parse them to get noisy unstructured text, clean it, format information (by adding several tags) and finally, transform it into semi-structured text, where sentence boundaries are well marked.
Refer to Accepted Papers in FinNLP 2019
In addition to an improved version of the previously proposed task, this task is extended to include the detection of lists and list items, as well as their hierarchy in this year.
FinSBD'2 is split into two sub-tasks:
- Extracting sentence boundaries, including list and list item boundaries.
- Organizing the lists items hierarchically.
This task is to classify a sentence/comment with blank where should be put in a numerical value into eight classes by their magnitudes. The distribution of different magnitudes is,
| Magnitude | Range | Ratio |
|---|---|---|
| Decimal | 0 ≤ m < 1 | 0.14 |
| 1 | 1 ≤ m < 10 | 2.50 |
| 2 | 10 ≤ m < 100 | 38.5 |
| 3 | 100 ≤ m < 1000 | |
| 4 | 1000 ≤ m < 10000 | |
| 5 | 10000 ≤ m < 100000 | |
| 6 | 100000 ≤ m <1000000 | |
| 7 | 1000000 ≤ m < + |
The sizes of the training set and test set are 500k and 100k, respectively.
In this task, participants will be given a list of carefully selected terms from the Financial domain such as “European depositary receipt”, “Interest rate swaps” and will be asked to design a system which can automatically classify them into the most relevant hypernym (or top-level) concept in an external ontology. For example, given the set of concepts “Bonds”, “Unclassified”, “Share”, “Loan”, the most relevant hypernym of “European depositary receipt” is “Share”.
Participants will be given a large corpus of in-domain data to facilitate learning semantic representations as well as a set of concepts extracted from an ontology (The Financial Industry Business Ontology (FIBO)).
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Reuters Corpora (RCV1, RCV2, TRC2)
- RCV1: Reuters Corpus Volume 1, contains about 810,000 Reuters, English Language News stories
- RCV2: Reuters Corpus Volume 2, contains over 487,000 Reuters News stories in thirteen languages (Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish).
- TRC2: Thomson Reuters Text Research Collection, comprises 1,800,370 news stories covering the period from 2008-01-01 00:00:03 to 2009-02-28 23:54:14.
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US. Electronic filings for each company each year.
- FinBERT: Language model trained on
TRC2Link - FinBERT: Pre-Trained on SEC Filings for Financial NLP Tasks on
Fin10k, Link
- Stock Price Prediction
- Customer Engagment
- Risk Management
- Compliance Check
- TBD