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Generation of Interpreted Vector Representations of Words Based on Supersenses
AbstractThis work presents an approach to creating interpreted vector representations of words in which each component of the vector corresponds to a...
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CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection
ContextCode smell detection is the process of identifying poorly designed and implemented code pieces. Machine learning-based approaches require...
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Fine-Tuning Transformer-Based Representations in Active Learning for Labelling Crisis Dataset of Tweets
Supervised machine learning-based models are generally used for classifying tweets related to crisis. A labelled tweet dataset is a major requirement...
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Hardware Implementation of Code Converters Designed to Reduce the Length of Binary Encoded Words
AbstractThe problems of synthesis of combinational circuits of code converters designed to reduce the length of words from a given set of encoded...
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Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences
Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive...
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Vector Retrieval
This chapter sets the stage for the remainder of this monograph. It explains where vectors come from, how they have come to represent data of any...
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Large Sentiment Dictionary of Russian Words
Sentiment analysis is a widely studied area of computational linguistics. The main tool for sentiment analysis of texts are dictionaries with...
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Counting Words
As we have seen in Chapters 2 to 7, language has a fabulous, partly-visible, and partly-hidden structure, and still a lot remains to be discovered....
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Learning contextual representations for entity retrieval
In this paper, we introduce Contextual Entity Ranking (CoER) for the task of entity retrieval. CoER utilizes a textual knowledge graph to learn...
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Learning Sentiment-Enhanced Word Representations by Fusing External Hybrid Sentiment Knowledge
Word representation learning is a fundamental technique in cognitive computation that plays a crucial role in enabling machines to understand and...
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Effective inter-aspect words modeling for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a prominent and challenging issue in natural language processing tasks. It aims to analyze the emotion of...
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Interpretable representations in explainable AI: from theory to practice
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and...
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Composing Word Vectors for Japanese Compound Words Using Dependency Relations
The use of distributed representations, e.g., via word2vec, has become popular in recent years. However, Japanese has many compound words and we...
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Unbinding tensor product representations for image captioning with semantic alignment and complementation
Image captioning, which describes an image with natural language, is an important but challenging multi-modal task. Many state-of-the-art methods...
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Efficient Multi-vector Dense Retrieval with Bit Vectors
Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These...
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Recognition of score words in freestyle kayaking using improved DTW matching
Voice is the most natural information carrier for human beings, and is likely to become the main method of human–computer interaction in the future....
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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network
The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social...
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Embedding generation for text classification of Brazilian Portuguese user reviews: from bag-of-words to transformers
Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service....
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A controlled experiment of different code representations for learning-based program repair
Training a deep learning model on source code has gained significant traction recently. Since such models reason about vectors of numbers, source...
