Tootfinder

@netzschleuder@social.skewed.de
2025-11-01 00:00:04

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words: Bag of words (2008). 10336 nodes, 353160 edges. https://networks.skewed.de/net/bag_of_words#kos

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-10-30 13:00:05

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 13919 nodes and 746316 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words: Bag of words (2008). 13919 nodes, 746316 edges. https://networks.skewed.de/net/bag_of_words#nips

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-10-24 17:00:04

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-11-20 23:00:05

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@publicvoit@graz.social
2025-10-15 12:29:41

Inner #feelings of a vomit bag.

a picture of a vomit bag in an airplane with printed words: I wish I was a popcorn bag.

@netzschleuder@social.skewed.de
2025-12-12 11:00:04

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-10-05 11:00:05

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 67963 nodes and 3710420 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://…

bag_of_words: Bag of words (2008). 67963 nodes, 3710420 edges. https://networks.skewed.de/net/bag_of_words#enron

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-11-03 22:00:04

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@netzschleuder@social.skewed.de
2025-12-02 20:00:05

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 13919 nodes and 746316 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words — Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. After tokenization and removal of stopwords, the vocabulary of unique words was truncated by only keeping words that occurred more than ten times. Individual document names (i.e. a identifier for each docID) are not provided for copyright reasons.

@arXiv_csCL_bot@mastoxiv.page
2025-10-06 10:17:09

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Tien Phat Nguyen, Vu Minh Ngo, Tung Nguyen, Linh Van Ngo, Duc Anh Nguyen, Sang Dinh, Trung Le
https://arxiv.org/abs/2510.02788

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and neural approaches. While previous methods have achieved some improvements in topic diversity, they often struggle to ensure high topic coherence and consistent alignment across languages. We propose XTRA (Cross-Lingual Topic Modeling with Topic and Representation Alignments), a novel framework that unifies Bag-of-Words …

Tootfinder

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