Location Identification, Extraction and Disambiguation using Machine Learning in Legal Contracts

Joshi, Sandeep; Shah, Parth; Pandey, Amaresh Kumar

doi:10.1109/ccaa.2018.8777631

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…A range of information extraction techniques are proposed for particular applications, containing metadata extraction from scientific journals [3], legal contract entity extraction [100], [84], receipt entity extraction [59], and clinical text extraction [93], [20]. It is quite challenging to design a general-purpose text information extraction system as there are a lot of variations in a document image.…”

Section: Rq4-ai Approaches Used For Unstructured Document Processingmentioning

confidence: 99%

“…For example, in [43], a BoW is used for business invoice recognition and to capture layout and textual properties for interested fields. • Term Frequency-Inverse Document Frequency (TF-IDF): It calculates the word occurrence/frequency referred to as "Term Frequency" inside the entire document, against the word occurrence/frequency count inside the document corpus [100]. In TF-IDF, weights are assigned to words.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

“…It means they work well for specific use cases, but not for generalized-task [24], [2]. 2) Learning-based approaches: Few studies have discussed the learning-based approaches [10], [2], [100]. These methods are used to substitute the humandefined rules, that are necessary for a rule-based category.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

“…It means certain data is already labeled with the correct solution. Few supervised learning-based methods reported in the literature are Hidden Markov Model (HMM) [100], [12], SVM [2], [100], Decision Trees [61], and Naive Bayesian methods [10], [81], Conditional Random Fields (CRF) [84], [11]. Semisupervised learning algorithms iteratively apply a supervised learning algorithm with both labeled data and unlabeled data.…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

“…The classifier learns to classify unlabeled data based on the knowledge of the labeled data. A semi-supervised model aims to classify some of the unlabeled data using the labeled data [100].…”

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Section: Rq4-ai Approaches Used For Unstructured Document Processingmentioning

confidence: 99%

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

Section: ) Named Entity Recognition (Ner)mentioning

confidence: 99%

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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A Review of Contract Entity Extraction

Aejas

Bouras

Belhi

et al. 2021

Proceedings of Sixth International Congress on Information and Communication Technology

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Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

2021

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The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and highquality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for endto-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multilayout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model. INDEX TERMS Artificial Intelligence (AI), information extraction, key field extraction, Named Entity Recognition (NER), template-free invoice processing, unstructured data.

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Location Identification, Extraction and Disambiguation using Machine Learning in Legal Contracts

Cited by 8 publications

References 6 publications

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

A Review of Contract Entity Extraction

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

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