Distributional Hypothesis
Related Papers
Noun Classification From Predicate-Argument Structures
A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. …...
Automatic Noun Classification by Using Japanese-English Word Pairs
This paper describes a method of classifying semantically similar nouns. The approach is based on the "distributional hypothesis". Our approach is characterized by distinguishing among senses of the same word …...
Evaluation of Technology Term Recognition with Random Indexing
AbstractIn this paper, we propose a method that combines the principles of automatic term recognition and the distributional hypothesis to identify technology terms from a corpus of scientific publications. We …...
Learning to Predict Distributions of Words Across Domains
Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been lim- ited to a single domain because the dis- tribution …...
Reducing data sparsity by generalising distributional contexts: application to specialised texts (Réduction de la dispersion des données par généralisation des contextes distributionnels : application aux textes de spécialité) [in French]
. Vector space models implement the distributional hypothesis relying on the repetition of information oc- curring in the contexts of words to associate. However, these models suffer from a high …...
Testing Distributional Hypothesis in Patent Translation
This paper presents a wordlist-based lexical richness approach to testing distributional hypothesis for genre analysis in translation studies. In recent years, there has been continuing interest in patent translation. However, …...
Exploitation of Co-reference in Distributional Semantics
AbstractThe aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that …...
Learning Emotion-enriched Word Representations
AbstractMost word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar …...
Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation
Distributional hypothesis has been playing a central role in statistical NLP. Recently, however, its limitation in incorporating perceptual and empirical knowledge is noted, eliciting a field of perceptually grounded computational …...
Methods for Numeracy-Preserving Word Embeddings
AbstractWord embedding models are typically able to capture the semantics of words via the distributional hypothesis, but fail to capture the numerical properties of numbers that appear in the text. …...
Emotion Enriched Retrofitted Word Embeddings
AbstractWord embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) are good at encoding various lexical-semantic relations. However, they do not capture the emotion aspects of words. We present a …...
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models
AbstractNatural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning. We propose that n-grams composed of random character …...