Elucidating Conceptual Properties from Word Embeddings
In this paper, we introduce a method of identifying the components (i.e. dimensions) of word embeddings that strongly signifies properties of a word. By elucidating such properties hidden in word embeddings, we could make word embeddings more interpretable, and also could perform property-based meaning comparison. With the capability, we can answer questions like {`}To what degree a given word has the property cuteness?{''} or {`}In what perspective two words are similar?{''}. We verify our method by examining how the strength of property-signifying components correlates with the degree of prototypicality of a target word.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingNamed Entity Recognition (NER)Sentiment AnalysisWord EmbeddingsSimilar Papers 제목 키워드 기반
Embedding Word Similarity with Neural Machine Translation
Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-devel…
Language ModelingLanguage ModellingMachine TranslationTranslation+1$K$-Embeddings: Learning Conceptual Embeddings for Words using Context
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Vision transformers (ViTs) have emerged as a significant area of focus, particularly for their capacity to be jointly trained with large language models and to serve as robust vision foundation models. Yet, the developme…
From the New World of Word Embeddings: A Comparative Study of Small-World Lexico-Semantic Networks in LLMs
Lexico-semantic networks represent words as nodes and their semantic relatedness as edges. While such networks are traditionally constructed using embeddings from encoder-based models or static vectors, embeddings from d…
DecoderWord EmbeddingsBERT's Conceptual Cartography: Mapping the Landscapes of Meaning
Conceptual Engineers want to make words better. However, they often underestimate how varied our usage of words is. In this paper, we take the first steps in exploring the contextual nuances of words by creating conceptu…
Word Embeddings