Papers Word Embeddings
“Word Embeddings” 태그가 달린 논문 4,002편 · 필터 해제
Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turn…
Machine TranslationSign Language TranslationTranslationWord EmbeddingsComputational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models
Identifying parallel passages in biblical Hebrew is foundational in biblical scholarship for uncovering intertextual relationships. Traditional methods rely on manual comparison, which is labor-intensive and prone to hum…
Word EmbeddingsIncluding Semantic Information via Word Embeddings for Skeleton-based Action Recognition
Effective human action recognition is widely used for cobots in Industry 4.0 to assist in assembly tasks. However, conventional skeleton-based methods often lose keypoint semantics, limiting their effectiveness in comple…
Action RecognitionSkeleton Based Action RecognitionTemporal Action LocalizationWord EmbeddingsLow-resource keyword spotting using contrastively trained transformer acoustic word embeddings
We introduce a new approach, the ContrastiveTransformer, that produces acoustic word embeddings (AWEs) for the purpose of very low-resource keyword spotting. The ContrastiveTransformer, an encoder-only model, directly op…
Keyword SpottingWord EmbeddingsCharacterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
Measuring how semantics of words change over time improves our understanding of how cultures and perspectives change. Diachronic word embeddings help us quantify this shift, although previous studies leveraged substantia…
ArticlesDiachronic Word EmbeddingsSentiment AnalysisWord EmbeddingsLearning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premi…
Word EmbeddingsRecommender systems, stigmergy, and the tyranny of popularity
Scientific recommender systems, such as Google Scholar and Web of Science, are essential tools for discovery. Search algorithms that power work through stigmergy, a collective intelligence mechanism that surfaces useful …
Recommendation SystemsWord EmbeddingsStatic Word Embeddings for Sentence Semantic Representation
We propose new static word embeddings optimised for sentence semantic representation. We first extract word embeddings from a pre-trained Sentence Transformer, and improve them with sentence-level principal component ana…
Contrastive LearningKnowledge DistillationSentenceWord EmbeddingsCross-Domain Bilingual Lexicon Induction via Pretrained Language Models
Bilingual Lexicon Induction (BLI) is generally based on common domain data to obtain monolingual word embedding, and by aligning the monolingual word embeddings to obtain the cross-lingual embeddings which are used to ge…
Bilingual Lexicon InductionWord EmbeddingsWord TranslationOn the Emergence of Linear Analogies in Word Embeddings
Models such as Word2Vec and GloVe construct word embeddings based on the co-occurrence probability $P(i,j)$ of words $i$ and $j$ in text corpora. The resulting vectors $W_i$ not only group semantically similar words but …
AttributeWord EmbeddingsBridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation
Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state information (CSI) feedback. However, existin…
Knowledge DistillationPredictionTime Series PredictionWord EmbeddingsA Comparative Analysis of Static Word Embeddings for Hungarian
This paper presents a comprehensive analysis of various static word embeddings for Hungarian, including traditional models such as Word2Vec, FastText, as well as static embeddings derived from BERT-based models using dif…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+4An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition
Studies of morphological processing have shown that semantic transparency is crucial for word recognition. Its computational operationalization is still under discussion. Our primary objectives are to explore embedding-b…
Word EmbeddingsTowards Smart Point-and-Shoot Photography
Hundreds of millions of people routinely take photos using their smartphones as point and shoot (PAS) cameras, yet very few would have the photography skills to compose a good shot of a scene. While traditional PAS camer…
Mixture-of-ExpertsWord EmbeddingsWord Embedding Techniques for Classification of Star Ratings
Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of the…
ClassificationDimensionality ReductionFeature Engineeringtext-classification+2Word Embeddings Track Social Group Changes Across 70 Years in China
Language encodes societal beliefs about social groups through word patterns. While computational methods like word embeddings enable quantitative analysis of these patterns, studies have primarily examined gradual shifts…
Diachronic Word EmbeddingsWord EmbeddingsGeological Inference from Textual Data using Word Embeddings
This research explores the use of Natural Language Processing (NLP) techniques to locate geological resources, with a specific focus on industrial minerals. By using word embeddings trained with the GloVe model, we extra…
BenchmarkingWord EmbeddingsLayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers inve…
Dimensionality Reductiontext similarityWord EmbeddingsInvestigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations
Large Language Models (LLMs) have demonstrated unprecedented language understanding and reasoning capabilities to capture diverse user preferences and advance personalized recommendations. Despite the growing interest in…
FairnessRecommendation SystemsWord EmbeddingsmyNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging
Named Entity Recognition (NER) involves identifying and categorizing named entities within textual data. Despite its significance, NER research has often overlooked low-resource languages like Myanmar (Burmese), primaril…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+5