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Found 24 papers, 7 papers with code
Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically
This paper presents a method for semi-automatically building a corpus of full-text English-language biomedical articles annotated with part-of-speech tags.
BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles
This paper suggests a mechanism for linking citing sentences in a publication with cited sentences in referenced sources.
Method Entity Extraction from Biomedical Texts
Using this silver standard corpus we train two machine learning algorithms to automatically extract method entities from biomedical text.
MeSHup: Corpus for Full Text Biomedical Document Indexing
Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.
Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis
The tendency for claims to use other claims as their supporting evidence in addition to the experimental data led to two novel models that have provided a better understanding of the large scale argumentation structure of a complete biochemistry paper.
A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students’ Persuasive Essays
We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation—in an end-to-end machine learning pipeline.
Building a Synthetic Biomedical Research Article Citation Linkage Corpus
In this paper, to find these citation linkages in biomedical research publications using deep learning, we provide a synthetic silver standard corpus as well as the method to build this corpus.
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained.
MeSHup: A Corpus for Full Text Biomedical Document Indexing
Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.
Evaluation Benchmarks for Spanish Sentence Representations
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years.
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling
Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information.
Multilingual Corpus Creation for Multilingual Semantic Similarity Task
In this work, we describe a semi-automated framework to create a multilingual corpus which can be used for the multilingual semantic similarity task.
A Lexicon-Based Approach for Detecting Hedges in Informal Text
Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases.
Annotation of Rhetorical Moves in Biochemistry Articles
In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset.
Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing
The goal of text classification is to automatically assign categories to documents.
You Only Need Attention to Traverse Trees
To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention.
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition
It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily.
Improving Tree-LSTM with Tree Attention
In Natural Language Processing (NLP), we often need to extract information from tree topology.
A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation
Word sense disambiguation (WSD) is a well researched problem in computational linguistics.
Improving Neural Sequence Labelling using Additional Linguistic Information
Sequence labelling is the task of assigning categorical labels to a data sequence.
Extracting Connected Concepts from Biomedical Texts using Fog Index
We rank sentences of a text according to their FI and select 30 percent of the most difficult sentences.
