Papers by Paul Landes

3 papers
RoBERTa Low Resource Fine Tuning for Sentiment Analysis in Albanian (2024.lrec-main)

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Challenge: Recent advances in the education domain have provided new opportunities for solving interesting, but difficult problems.
Approach: They propose to use EduSenti to fine-tune language models for assigning sentiment to reviews of educators' performance annotated for sentiment, emotion and educational topic.
Outcome: The proposed model is compared with an Albanian masked language trained model from the last XLM-RoBERTa checkpoint and shows that it is a good fit for the proposed model.
A New Public Corpus for Clinical Section Identification: MedSecId (2022.coling-1)

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Challenge: a study aims to segment sections of clinical medical domain documentation . section identification is a process by which sections are demarcated and labeled .
Approach: They use a set of 2,002 fully annotated medical notes from the MIMIC-III to segment sections in clinical medical domain documentation.
Outcome: The proposed model shows that medical concepts are related across sections using principal component analysis.
CALAMR: Component ALignment for Abstract Meaning Representation (2024.lrec-main)

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Challenge: Abstract meaning representation (AMR) graphs represent semantic structure in a syntactic independent way.
Approach: They propose a method for graph alignment that can support summarization and evaluation.
Outcome: The proposed method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners.

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