Search Results for author: Frank Schilder

Found 14 papers, 0 papers with code

Making a Computational Attorney

no code implementations • 7 Mar 2023 • Dell Zhang, Frank Schilder, Jack G. Conrad, Masoud Makrehchi, David von Rickenbach, Isabelle Moulinier

This "blue sky idea" paper outlines the opportunities and challenges in data mining and machine learning involving making a computational attorney -- an intelligent software agent capable of helping human lawyers with a wide range of complex high-level legal tasks such as drafting legal briefs for the prosecution or defense in court.

Language Modelling

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TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data

no code implementations • 7 Dec 2022 • Beichen Zhang, Frank Schilder, Kelly Helm Smith, Michael J. Hayes, Sherri Harms, Tsegaye Tadesse

The model was then applied to California tweets and validated with keyword-based labels.

Transfer Learning

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Legal Prompt Engineering for Multilingual Legal Judgement Prediction

no code implementations • 5 Dec 2022 • Dietrich Trautmann, Alina Petrova, Frank Schilder

Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill.

Language Modelling Large Language Model +1

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Legal Prompting: Teaching a Language Model to Think Like a Lawyer

no code implementations • 2 Dec 2022 • FangYi Yu, Lee Quartey, Frank Schilder

Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering.

Common Sense Reasoning Language Modelling +2

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Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets

no code implementations • WS 2019 • Thomas Vacek, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, Frank Schilder

Dockets contain a wealth of information for planning a litigation strategy, but the information is locked up in semi-structured text.

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Litigation Analytics: Extracting and querying motions and orders from US federal courts

no code implementations • NAACL 2019 • Thomas Vacek, Dezhao Song, Hugo Molina-Salgado, Ronald Teo, Conner Cowling, Frank Schilder

In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.

BIG-bench Machine Learning Question Answering

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The E2E NLG Challenge: A Tale of Two Systems

no code implementations • WS 2018 • Charese Smiley, Elnaz Davoodi, Dezhao Song, Frank Schilder

This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system.

Text Generation Vocal Bursts Valence Prediction

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Finding the ``right'' answers for customers

no code implementations • WS 2017 • Frank Schilder

This talk will present a few NLG systems developed within Thomson Reuters providing information to professionals such as lawyers, accountants or traders.

Text Generation