KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports

Hillebrand, Lars; Deuser, Tobias; Dilmaghani, Tim; Kliem, Bernd; Loitz, Rüdiger; Bauckhage, Christian; Sifa, Rafet

doi:10.1109/icpr56361.2022.9956191

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…We believe those studies will benefit from going beyond structured data. Hillebrand et al [28] fine-tuned BERT to identify specific concepts such as key performance indicators (kpi), current year monetary value (cy), and davon, that are defined by the authors. In contrast to building a graph, their focus is on analyzing business documents, which is our domain of study, and they demonstrate an approach in handling specific entities within unstructured text documents.…”

Section: Business Network Knowledge Graph Construction and Analysismentioning

confidence: 99%

Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis

Gohourou,

Kuwabara

2024

MAKE

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Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful networks is the abundant amount of unstructured text data available through the web. The efforts to turn unstructured text data into a network have spawned different research endeavors, including the simplification of the process. This study presents the design and implementation of TraCER, a pipeline that turns unstructured text data into a graph, targeting the business networking domain. It describes the application of natural language processing techniques used to process the text, as well as the heuristics and learning algorithms that categorize the nodes and the links. The study also presents some simple yet efficient methods for the entity-linking and relation classification steps of the pipeline.

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Section: Business Network Knowledge Graph Construction and Analysismentioning

confidence: 99%

Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis

Gohourou,

Kuwabara

2024

MAKE

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“…For example, Ebert et al [26] proposed segment alignment at the output of joint extraction of entity relations, which alleviates the high complexity problem of entity recognition. Hillebrand et al [27] designed an end-to-end trainable architecture that combines recurrent neural networks with conditional label masks. Lai et al [28] built an initial domain graph and reasoned collectively in order to achieve joint extraction.…”

Section: Knowledge Extractionmentioning

confidence: 99%

“…Hillebrand et al [27] recursive neural networks combined with conditional label masks simplifies the extraction task Lai et al [28] collective reasoning to achieve joint extraction Carbonell et al [29] supervised messaging joint extraction of entity relationships semi-structured document extraction…”

Section: Knowledge Extractionmentioning

confidence: 99%

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Qu,

Zhang,

Liang

et al. 2023

Electronics

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The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment.

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“…A capsule network for the detection of fraud in accounting reports was proposed by [36]. [14] developed a joint named entity and relation extraction model based on BERT to extract key performance indicators and their numerical values from a corpus of German financial reports. [8] applied a similar approach to reports from the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, a platform hosted by the U.S. Securities and Exchange Commission, and published their dataset along with their results.…”

Section: Related Workmentioning

confidence: 99%

“…We train each model variation for 15 epochs and determine its best checkpoint via early stopping 14 . For the custom Part-Of-Speech tagging pre-training, the model is being trained for a maximum of 25 epochs, as we observe that convergence happens slower than during fine-tuning.…”

Section: Training Setupmentioning

confidence: 99%

Contradiction Detection in Financial Reports

Deußer

Pielka²,

Pucknat³

et al. 2023

nldl

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Finding and amending contradictions in a financial report is crucial for the publishing company and its financial auditors. To automate this process, we introduce a novel approach that incorporates informed pre-training into its transformer-based architecture to infuse this model with additional Part-Of-Speech knowledge. Furthermore, we fine-tune the model on the public Stanford Natural Language Inference Corpus and our proprietary financial contradiction dataset. It achieves an exceptional contradiction detection F1 score of 89.55% on our real-world financial contradiction dataset, beating our several baselines by a considerable margin. During the model selection process we also test various financial-document-specific transformer models and find that they underperform the more general embedding approaches.

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KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports

Cited by 22 publications

References 19 publications

Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis

Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

Contradiction Detection in Financial Reports

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