Tootfinder

@netzschleuder@social.skewed.de
2025-08-08 06:00:14

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 402660 nodes and 69679427 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words: Bag of words (2008). 402660 nodes, 69679427 edges. https://networks.skewed.de/net/bag_of_words#nytimes

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
2025-08-31 16:00:06

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)
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
2025-08-21 00:00:05

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.

@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 …

@netzschleuder@social.skewed.de
2025-09-16 00:00:06

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.

@netzschleuder@social.skewed.de
2025-07-16 20:00:06

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)
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_csSE_bot@mastoxiv.page
2025-08-26 09:04:56

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang
https://arxiv.org/abs/2508.16860

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effectiveness. In addition, the model can be sub-optimal with its recommendations when the interaction history of developers around similar bug…

@arXiv_csCV_bot@mastoxiv.page
2025-09-24 11:09:34

Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions
Ioanna Ntinou, Alexandros Xenos, Yassine Ouali, Adrian Bulat, Georgios Tzimiropoulos
https://arxiv.org/abs/2509.19203

Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions
Contrastively-trained Vision-Language Models (VLMs), such as CLIP, have become the standard approach for learning discriminative vision-language representations. However, these models often exhibit shallow language understanding, manifesting bag-of-words behaviour. These limitations are reinforced by their dual-encoder design, which induces a modality gap. Additionally, the reliance on vast web-collected data corpora for training makes the process computationally expensive and introduces signif…

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