"Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data."
A method of analyzing meaning based on the statistical distribution of words in a corpus.
Word embeddings: A technique for representing words in a high-dimensional vector space that captures the words' meanings and relationships to other words based on their co-occurrence patterns in large text corpora.
Distributional similarity: A measure of how similar two words are based on their distributional patterns in a corpus.
Context: The surrounding words or phrases that provide clues about the meaning of a word.
Corpus: A large collection of texts used for studying language and building distributional semantic models.
Co-occurrence: The frequency with which two words appear together in a corpus.
Dimension reduction: Techniques for reducing the high-dimensional vector space of word embeddings to a lower-dimensional space while retaining the most important information.
Vector arithmetic: Operations on word vectors that allow for analogical reasoning, such as finding the "opposite" of a word or predicting the word that completes a given analogy.
Neural networks: Machine learning models used for training distributional semantic models, such as word2vec and GloVe.
Evaluation metrics: Measures used to assess the performance of distributional semantic models, such as accuracy on word similarity or analogy tasks.
Domain adaptation: Techniques for adapting distributional semantic models to a specific domain or task, such as sentiment analysis or named entity recognition.
Polysemy: The phenomenon of a single word having multiple meanings, which can be disambiguated using distributional semantic models.
Part-of-speech tagging: The task of automatically labeling each word in a corpus with its part of speech, which can be used to improve distributional semantic models.
Semantic change: The phenomenon of words' meanings evolving over time or across different contexts, which can be studied using distributional semantic models.
Multilingualism: The use of distributional semantic models to represent words and concepts across multiple languages, which can aid in tasks such as machine translation and cross-lingual information retrieval.
Co-occurrence-based distributional semantics: This approach uses the co-occurrence of words in a large corpus to create distributional models of words and their relationships.
collocation-based distributional semantics: This approach focuses on identifying and analyzing frequently occurring patterns of words, known as collocations, in a corpus.
Distributional semantics with neural networks: This approach uses artificial neural networks to automatically learn the semantic relationships between words, based on their distributional patterns in a corpus.
Latent semantic analysis: This technique uses linear algebra and dimensionality reduction to represent the meaning of words and documents in a lower-dimensional space, based on their co-occurrences in a corpus.
Distributional semantics with probabilistic models: This approach uses probabilistic models such as topic models and latent Dirichlet allocation to discover hidden topics and meaningful patterns in large corpora.
Distributional semantics with vector space models: This approach represents words as vectors in a high-dimensional space, where their distance and similarity can be measured based on their co-occurrences in a corpus.
Distributional clustering: This technique groups similar words together based on their distributional patterns in a corpus, using unsupervised clustering algorithms such as k-means or hierarchical clustering.
Distributional semantics with graph models: This approach represents the relationships between words as a graph, where nodes are words and edges represent their co-occurrences in a corpus.
Distributional semantics with ontologies: This approach uses structured knowledge representations such as ontologies and semantic networks to capture the hierarchical and conceptual relationships between words and concepts.
"Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items."
"The basic idea of distributional semantics can be summed up in the so-called distributional hypothesis: linguistic items with similar distributions have similar meanings."
"Theories and methods for quantifying and categorizing semantic similarities between linguistic items..."
"Distributional semantics analyzes linguistic items based on their distributional properties..."
"...distributional properties in large samples of language data."
"Distributional semantics develops and studies theories and methods for quantifying and categorizing semantic similarities..."
"The basic idea of distributional semantics can be summed up in the so-called distributional hypothesis..."
"Linguistic items with similar distributions have similar meanings."
"The basic idea of distributional semantics can be summed up in the so-called distributional hypothesis..."
"Distributional semantics is a research area that develops and studies theories and methods..."
"Distributional properties in large samples of language data."
"Distributional semantics...quantifying and categorizing semantic similarities..."
"Distributional semantics...categorizing semantic similarities..."
"Theories and methods for quantifying and categorizing semantic similarities..."
"Linguistic items..."
"...semantic similarities..."
"Large samples of language data."
"The basic idea of distributional semantics can be summed up in the so-called distributional hypothesis..."
"Linguistic items with similar distributions have similar meanings."