Papers with k-means
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation (N19-1)
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| Challenge: | a novel word embedding representation for text documents is proposed . the method is based on the Vector of Locally-Aggregated Descriptors used for image representation . |
| Approach: | They propose a novel representation for text documents based on aggregating word embedding vectors into document embeddables. |
| Outcome: | The proposed representation improves on the Movie Review data set and is 10% better than the state-of-the-art representation. |
FANATIC: FAst Noise-Aware TopIc Clustering (2021.findings-emnlp)
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| Challenge: | a large amount of data can be computationally prohibitive for extracting topic noise . many clustering algorithms assign documents to one of the available clusters . a novel algorithm that efficiently distinguishes documents from genuine topics is developed . |
| Approach: | They propose an algorithm that efficiently distinguishes documents from genuine topics . they use a reddit dataset to showcase the algorithm as it contains short, noisy data . |
| Outcome: | The proposed algorithm outperforms hdbscan and hANATIC on a Twitter dataset. |
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set (2021.eacl-main)
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| Challenge: | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. |
| Approach: | They introduce a Large Romanian Sentiment Data Set which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. |
| Outcome: | The proposed data set is composed of 15,000 positive and negative reviews from the largest Romanian e-commerce platform. |
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition (2020.lrec-1)
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| Challenge: | despite the extensive use of HMMs for sign recognition, determining the HMM structure remains a challenge . despite their success in modeling sequential and multivariate data, establishing the structure remains challenging . |
| Approach: | They propose a continuous HMM framework for modeling and recognizing isolated signs . they propose to optimize the number of states for each sign separately during recognition . |
| Outcome: | The proposed model performs better on three different datasets and is competitive with existing models. |