Distributional Document Embedding Space
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A Distributional Document Embedding Space is a text-item embedding space that is a text-item embedding space composed of distributional document vectors.
- Context:
- It can (often) have a Distributional Document Vectorizing Function.
- It can be created by a Distributional Document Embedding Modeling System (that implements a distributional documenbt embedding modeling algorithm).
- It can range from being a Closed Distributional Document Vector Space Model (that applies only to the words in the training data) to being an Open Distributional Document Vector Space Model.
- …
- Example(s):
- Counter-Example(s):
- See: Lexical Co-Occurrence Matrix, Distributional Word Vector, Vector Space Model.
References
2015a
- (Dai et al., 2015) ⇒ Andrew M. Dai, Christopher Olah, and Quoc V. Le. (2015). “Document Embedding with Paragraph Vectors.” In: NIPS Deep Learning Workshop.
2015b
- (Kusner et al., 2015) ⇒ Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. (2015). “From Word Embeddings to Document Distances.” In: Proceedings of the International Conference on Machine Learning, pp. 957-966.
- QUOTE: … We present the Word Mover's Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representations for words from local cooccurrences in sentences. …