This document provides an introduction and overview of distributional semantic models (DSMs). It discusses the distributional hypothesis, which states that the meaning of a word can be determined by the contexts in which it occurs. Several examples are given to illustrate how the meaning of an unknown word can be inferred from its distributions and neighbors in a high-dimensional space representing word co-occurrence statistics. Geometric interpretations of DSMs are presented, where words are represented as vectors in a space and similarity corresponds to spatial proximity. The tutorial will continue with a taxonomy of DSM parameters and techniques for using and evaluating DSMs.
表形式データのために提案されたDNNをベースとしたモデルとXGBoostを比較した論文を解説。
DNNとXGBoostの両方を用いたアンサンブル学習が良い性能が出たという実験結果などを紹介します。
Shwartz-Ziv, Ravid, and Amitai Armon. "Tabular Data: Deep Learning is Not All You Need." arXiv preprint arXiv:2106.03253 (2021).
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
表形式データのために提案されたDNNをベースとしたモデルとXGBoostを比較した論文を解説。
DNNとXGBoostの両方を用いたアンサンブル学習が良い性能が出たという実験結果などを紹介します。
Shwartz-Ziv, Ravid, and Amitai Armon. "Tabular Data: Deep Learning is Not All You Need." arXiv preprint arXiv:2106.03253 (2021).
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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14. Introduction The distributional hypothesis
What is the meaning of “bardiwac”?
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34. Introduction General overview
A very brief history of DSM
Introduced to computational linguistics in early 1990s
following the probabilistic revolution (Schütze 1992, 1998)
Other early work in psychology (Landauer and Dumais 1997;
Lund and Burgess 1996)
36. Introduction General overview
A very brief history of DSM
Introduced to computational linguistics in early 1990s
following the probabilistic revolution (Schütze 1992, 1998)
Other early work in psychology (Landauer and Dumais 1997;
Lund and Burgess 1996)
38. Introduction General overview
A very brief history of DSM
Introduced to computational linguistics in early 1990s
following the probabilistic revolution (Schütze 1992, 1998)
Other early work in psychology (Landauer and Dumais 1997;
Lund and Burgess 1996)
40. Introduction General overview
A very brief history of DSM
Introduced to computational linguistics in early 1990s
following the probabilistic revolution (Schütze 1992, 1998)
Other early work in psychology (Landauer and Dumais 1997;
Lund and Burgess 1996)
42. Introduction General overview
A very brief history of DSM
Introduced to computational linguistics in early 1990s
following the probabilistic revolution (Schütze 1992, 1998)
Other early work in psychology (Landauer and Dumais 1997;
Lund and Burgess 1996)
46. Introduction Three famous DSM examples
Latent Semantic Analysis (Landauer and Dumais 1997)
Corpus: 30,473 articles from Grolier’s Academic American
Encyclopedia (4.6 million words in total)
47. articles were limited to first 2,000 characters
Word-article frequency matrix for 60,768 words
row vector shows frequency of word in each article
Logarithmic frequencies scaled by word entropy
Reduced to 300 dim. by singular value decomposition (SVD)
borrowed from LSI (Dumais et al. 1988)
49. Introduction Three famous DSM examples
Word Space (Schütze 1992, 1993, 1998)
Corpus: ≈ 60 million words of news messages (New York
Times News Service)
Word-word co-occurrence matrix
20,000 target words & 2,000 context words as features
row vector records how often each context word occurs close
to the target word (co-occurrence)
co-occurrence window: left/right 50 words (Schütze 1998)
or ≈ 1000 characters (Schütze 1992)
Rows weighted by inverse document frequency (tf.idf)
Context vector = centroid of word vectors (bag-of-words)
91. Taxonomy of DSM parameters DSM parameters
Geometric vs. probabilistic interpretation
Geometric interpretation
row vectors as points or arrows in n-dim. space
very intuitive, good for visualisation
use techniques from geometry and linear algebra
Probabilistic interpretation
co-occurrence matrix as observed sample statistic
“explained” by generative probabilistic model
recent work focuses on hierarchical Bayesian models
probabilistic LSA (Hoffmann 1999), Latent Semantic
Clustering (Rooth et al. 1999), Latent Dirichlet Allocation
(Blei et al. 2003), etc.
explicitly accounts for random variation of frequency counts
intuitive and plausible as topic model