| # Financial Relation Extraction | |
| ## Process | |
| Detecting the presence of a relationship between financial terms and qualifying the relationship in case of its presence. Example use cases: | |
| * An A-B trust is a joint trust created by a married couple for the purpose of minimizing estate taxes. (<em>Relationship **exists**, type: **is**</em>) | |
| * There are no withdrawal penalties. (<em>Relationship **does not exist**, type: **x**</em>) | |
| ## Data | |
| The data consists of financial definitions collected from different sources (Wikimedia, IFRS, Investopedia) for financial indicators. Each definition has been split up into sentences, and term relationships in a sentence have been extracted using the [Stanford Open Information Extraction](https://nlp.stanford.edu/software/openie.html) module. | |
| A typical row in the dataset consists of a definition sentence and its corresponding relationship label. | |
| The labels were restricted to the 5 most-widely identified relationships, namely: **x** (no relationship), **has**, **is in**, **is** and **are**. | |
| ## Model | |
| The model used is a standard Roberta-base transformer model from the Hugging Face library. See [HUGGING FACE DistilBERT base model](https://huggingface.co/distilbert-base-uncased) for more details about the model. | |
| In addition, the model has been pretrained to initializa weigths that would otherwise be unused if loaded from an existing pretrained stock model. | |
| ## Metrics | |
| The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. | |
| | relation | precision | recall | f1-score | support | | |
| | ------------- |:-------------:|:-------------:|:-------------:| -----:| | |
| | has | 0.7416 | 0.9674 | 0.8396 | 2362 | | |
| | is in | 0.7813 | 0.7925 | 0.7869 | 2362 | | |
| | is | 0.8650 | 0.6863 | 0.7653 | 2362 | | |
| | are | 0.8365 | 0.8493 | 0.8429 | 2362 | | |
| | x | 0.9515 | 0.8302 | 0.8867 | 2362 | | |
| | | | | | | | |
| | macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 | | |
| | weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 | |