Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, Georgios Paliouras
| Challenge: | Publicly traded companies are required to submit periodic reports with eXtensive Business Reporting Language (XBRL) word-level tags. |
| Approach: | They propose to use XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1.1M sentences with gold X brl tags. |
| Outcome: | The proposed solution replaces numeric expressions with pseudo-tokens reflecting original token shapes and numeric magnitudes. |
Similar Papers
BUSTER: a “BUSiness Transaction Entity Recognition” dataset (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Natural Language Processing has seen major breakthroughs in the last few years, but transferring these advances into industry applications can be difficult. |
| Approach: | They propose to use a BUSiness Transaction Entity Recognition dataset to support industry-oriented research by exploiting both general-purpose and domain-specific language models. |
| Outcome: | The proposed model is the best performing model and an additional silver corpus to BUSTER. |
Financial Numeric Extreme Labelling: A dataset and benchmarking (2023.findings-acl)
Copied to clipboard
Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
| Challenge: | In 2019, the SEC mandates that all public companies file periodic financial statements that should contain numerals annotated with a particular label. |
| Approach: | They propose to use a dataset to automate the assignment of a label to a particular numeral span in a sentence from an extremely large label set. |
| Outcome: | The proposed solution outperforms the previous approaches but is less frequent than the pipeline solution. |
FinEntity: Entity-level Sentiment Classification for Financial Texts (2023.emnlp-main)
Copied to clipboard
| Challenge: | FinEntity annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
| Approach: | They introduce an entity-level sentiment classification dataset called FinEntity that annotates financial entity spans and their sentiment in financial news. |
| Outcome: | The proposed dataset annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fine-grained entity typing use weak labels that are automatically generated. |
| Approach: | They propose to obtain training data by using a BERT Masked Language Model (MLM) given a mention in a sentence, they construct an input for the MLM so it predicts context dependent hypernyms of the mention, which can be used as type labels. |
| Outcome: | The proposed model improves performance by using type labels generated from a BERT Masked Language Model given a mention in a sentence. |
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)
Copied to clipboard
Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable. |
| Approach: | They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA. |
| Outcome: | The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
Copied to clipboard
| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types . |
| Approach: | They propose to annotate 100 full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. |
| Outcome: | The proposed model can be used to identify 10 entity types in scientific articles . existing models cannot recognize fine-grained models like ML models and model architecture . |
Code and Named Entity Recognition in StackOverflow (2020.acl-main)
Copied to clipboard
| Challenge: | StackOverflow has 15 million programming related questions written by 8.5 million users . however, there is a lack of fundamental NLP resources and techniques for identifying software-related named entities within natural language sentences. |
| Approach: | They propose a named entity recognition corpus for the computer programming domain with 15,372 sentences annotated with 20 fine-grained entity types. |
| Outcome: | The proposed model improves on 152 million sentences from StackOverflow and achieves 79.10 F-1 score for code and named entity recognition. |
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition (2026.findings-eacl)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location" |
| Approach: | They propose a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. |
| Outcome: | The proposed model achieves comparable or improved performance in English, Thai, and Swahili despite being trained on 19x less data than strong baselines. |