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@d97hah suggested that we could also use Latent Semantic Analysis or Random Indexing to compare similarity between texts.
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# Background
So, I've suggested to integrate Distributional Semantics into the YodaQA pipeline by using JoBim Text, a framework developed by TU Darmstadt (in Germany) and IBM that is also used for do…
k0105 updated
8 years ago
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Dear developers,
I found that the VsmMain computes the `word-document matrix`, which concerns the co-occurrences of words and documents. Could I generate distributional representation using the conte…
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**word2vec**
> from http://xingjunjie.me/2017/08/07/Neural-Networks-from-Scratch/
…
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## 0. Paper
@article{arora-etal-2020-learning,
title = "Learning Lexical Subspaces in a Distributional Vector Space",
author = "Arora, Kushal and
Chakraborty, Aishik and
Che…
a1da4 updated
3 years ago
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**Title of the talk**
Introduction to word embeddings
**Description**
In this talk, we will take a look into what do we mean when we say, **what is the "meaning" of a certain word? and how that m…
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«Про мультимодальную семантику (и поминается книжка Geometry meaning), "Коннективистская весна онтологической инженерии", август 2016: http://ailev.livejournal.com/1283541.html
исследования по линии к…
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Please post here other relevant papers you can find on the detection of compositional nominal compounds (related to the task and the datasets in [1]). Some strong(er) baselines on the datasets Reddy, …
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## 집현전 최신반 스터디
- 2022년 5월 08일 일요일 10시 발표
- 김유빈님 오수지님 발표
- 논문 링크: https://arxiv.org/abs/2205.03815
> ### Abstract
> The logical negation property (LNP), which implies generating different predic…
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I'm reading your paper and have a question about section 3.2. Could you explain what are the vertex matrix and context matrix? Are they simply the source and the destination of edges?