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Found 23 papers, 1 papers with code
Causality extraction from medical text using Large Language Models (LLMs)
This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs).
Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers
The volume of scientific publications in organizational research becomes exceedingly overwhelming for human researchers who seek to timely extract and review knowledge.
Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement
Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening.
Causal Knowledge Extraction from Scholarly Papers in Social Sciences
The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge.
Topological Data Analysis in Text Classification: Extracting Features with Additive Information
While the strength of Topological Data Analysis has been explored in many studies on high dimensional numeric data, it is still a challenging task to apply it to text.
A Novel Method of Extracting Topological Features from Word Embeddings
In this paper, we introduce a novel algorithm to extract topological features from word embedding representation of text that can be used for text classification.
UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B
In this paper, we detail our submission to the 2019, 7th year, BioASQ competition.
Emotion Detection in Text: Focusing on Latent Representation
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc.
UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning
With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others.
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words
We show that fitting an emotional model into pre-trained word vectors can increase the performance of these models in emotional similarity metrics.
Topological Data Analysis for Discourse Semantics?
In this paper we present new results on applying topological data analysis to discourse structures.
UNCC QA: Biomedical Question Answering system
Our system uses extractive summarization techniques to generate answers and has scored highest ROUGE-2 and Rogue-SU4 in all test batch sets.
A Short Survey of Topological Data Analysis in Time Series and Systems Analysis
Topological Data Analysis (TDA) is the collection of mathematical tools that capture the structure of shapes in data.
Causality Analysis of Twitter Sentiments and Stock Market Returns
Sentiment analysis is the process of identifying the opinion expressed in text.
Emotion Detection in Text: a Review
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc.
A Sheaf Model of Contradictions and Disagreements. Preliminary Report and Discussion
We introduce a new formal model -- based on the mathematical construct of sheaves -- for representing contradictory information in textual sources.
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases
We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other.
Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines
We introduce a formal distinction between contradictions and disagreements in natural language texts, motivated by the need to formally reason about contradictory medical guidelines.
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
Sentiment analysis is the process of identifying the opinion expressed in text.
Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features
This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs.
Learning Concept Embeddings for Efficient Bag-of-Concepts Densification
Once they are learned, we propose an efficient vector aggregation method to generate fully continuous BoC representations.
Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data
The study positions MSA as one of state-of-the-art methods for measuring semantic relatedness, besides the inherent interpretability and simplicity of the generated semantic representation.
Watsonsim: Overview of a Question Answering Engine
The objective of the project is to design and run a system similar to Watson, designed to answer Jeopardy questions.
