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Found 24 papers, 0 papers with code
Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data
As one of the NER improvement approaches, multi-task learning that learns a model from multiple training data has been used.
Evaluating Hierarchical Document Categorisation
Hierarchical document categorisation is a special case of multi-label document categorisation, where there is a taxonomic hierarchy among the labels.
Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain
In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.
Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains
To address the issue, we propose a method to estimate the domain expertise of each annotator before the annotation process using information easily available from the annotators beforehand.
Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER
Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage.
Improving Neural Language Processing with Named Entities
Most neural-based NLP models receive only vectors encoded from a sequence of subwords obtained from an input text.
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
We propose a novel method to reduce the time of error detection.
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
Our contribution is four-fold: 1) we introduced nine vision-and-language (VL) tasks (including object recognition, image-text matching, and more) and constructed multilingual visual-text datasets in four languages: English, Japanese, Swahili, and Urdu through utilizing templates containing \textit{questions} and prompting GPT4-V to generate the \textit{answers} and the \textit{rationales}, 2) introduced a new VL task named \textit{unrelatedness}, 3) introduced rationales to enable human understanding of the VLM reasoning process, and 4) employed human evaluation to measure the suitability of proposed datasets for VL tasks.
Downstream Task-Oriented Neural Tokenizer Optimization with Vocabulary Restriction as Post Processing
This paper proposes an example of the BiLSTM-based tokenizer with vocabulary restriction, which can capture wider contextual information for the tokenization process than non-neural-based tokenization methods used in existing work.
Tokenization Preference for Human and Machine Learning Model: An Annotation Study
Is preferred tokenization for humans also preferred for machine-learning (ML) models?
On the (In)Effectiveness of Images for Text Classification
Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not.
Transformer-based Approach for Predicting Chemical Compound Structures
Instead of handcrafted rules, we propose Transformer-based models that predict SMILES strings from chemical compound names.
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model.
A Fast and Accurate Partially Deterministic Morphological Analysis
This paper proposes a partially deterministic morphological analysis method for improved processing speed.
Global Optimization under Length Constraint for Neural Text Summarization
The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6. 70{\%} overlength summaries on CNN/Daily and 7. 8{\%} on long summary of Mainichi, compared to the approximately 20{\%} to 50{\%} on CNN/Daily Mail and 10{\%} to 30{\%} on Mainichi with the other optimization methods.
Model Transfer with Explicit Knowledge of the Relation between Class Definitions
Instead of learning the individual classification layers for the support and target schemes, the proposed method converts the class label of each example on the support scheme into a set of candidate class labels on the target scheme via the class correspondence table, and then uses the candidate labels to learn the classification layer for the target scheme.
Detecting Heavy Rain Disaster from Social and Physical Sensor
We present our system that assists to detect heavy rain disaster, which is being used in real world in Japan.
An Eye-tracking Study of Named Entity Annotation
Utilising effective features in machine learning-based natural language processing (NLP) is crucial in achieving good performance for a given NLP task.
Big Community Data before World Wide Web Era
This paper introduces the NIFTY-Serve corpus, a large data archive collected from Japanese discussion forums that operated via a Bulletin Board System (BBS) between 1987 and 2006.
