Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- NLP (2)
- Natural language processing (2)
- AI RFP (1)
- Artificial Intelligence (1)
- Bert (1)
-
- Bert RFP (1)
- Bi-LSTM (1)
- Bidirectional Long Short-Term Memory networks (1)
- CRF (1)
- Conditional random fields (1)
- Court Dockets (1)
- DoD RFP (1)
- Document Analysis (1)
- Hugging Face (1)
- ML (1)
- Machine Learning (1)
- NER (1)
- NLP Emerging Technology (1)
- NLP RFP (1)
- Named entity recognition (1)
- Natural Language Processing (1)
- ODOC (1)
- OSCN (1)
- Oklahoma (1)
- Proposal Analysis (1)
- RFP NER (1)
- RFQ (1)
- RNN (1)
- Recurring Neural Networks (1)
- Request for Proposal (1)
Articles 1 - 3 of 3
Full-Text Articles in Law
Using Nlp To Model U.S. Supreme Court Cases, Katherine Lockard, Robert Slater, Brandon Sucrese
Using Nlp To Model U.S. Supreme Court Cases, Katherine Lockard, Robert Slater, Brandon Sucrese
SMU Data Science Review
The advantages of employing text analysis to uncover policy positions, generate legal predictions, and inform or evaluate reform practices are multifold. Given the far-reaching effects of legislation at all levels of society these insights and their continued improvement are impactful. This research explores the use of natural language processing (NLP) and machine learning to predictively model U.S. Supreme Court case outcomes based on textual case facts. The final model achieved an F1-score of .324 and an AUC of .68. This suggests that the model can distinguish between the two target classes; however, further research is needed before machine learning models …
Qualitative Leveraging Natural Language Processing To Establish Judge Incrimination Statistics To Educate Voters In Re-Elections, Aurian Ghaemmaghami, Paul Huggins, Grace Lang, Julia Layne, Robert Slater
Qualitative Leveraging Natural Language Processing To Establish Judge Incrimination Statistics To Educate Voters In Re-Elections, Aurian Ghaemmaghami, Paul Huggins, Grace Lang, Julia Layne, Robert Slater
SMU Data Science Review
The prevalence of data has given consumers the power to make informed choices based off reviews, ratings, and descriptive statistics. However, when a local judge is coming up for re-election there is not any available data that aids voters in making data-driven decision on their vote. Currently court docket data is stored in text or PDFs with very little uniformity. Scaling the collection of this information could prove to be complicated and tiresome. There is a demand for an automated, intelligent system that can extract and organize useful information from the datasets. This paper covers the process of web scraping …
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
SMU Data Science Review
Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …