| Challenge: | Recent advances in distributional semantics have led to the rise of neural network-based models that use unsupervised learning to represent words as dense, distributed vectors, called 'word embeddings' embedders hold key to improving natural language processing for low-resource languages, since they require significant time and manpower. |
| Approach: | They train a skip-gram model on 140 million Urdu words to create the first large-scale word embeddings for the Urdu language. |
| Outcome: | The proposed models capture high degree of syntactic and semantic similarity between words and are able to generalize well on the Urdu translation task. |
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| Challenge: | Existing word embeddings techniques have a fixed vocabulary, i.e., they can only provide vectors over a finite set of common words that appear frequently in a given corpus. |
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| Challenge: | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. |
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Tsetlin Machine Embedding: Representing Words Using Logical Expressions (2024.findings-eacl)
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| Challenge: | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing. |
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Learning Word Vectors for 157 Languages (L18-1)
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| Challenge: | Distributed word representations, or word vectors, have been used in natural language processing for many tasks. |
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Querying Word Embeddings for Similarity and Relatedness (N18-1)
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| Challenge: | Word2Vec embeddings have become popular representations of word meaning . similarity between two words is often assumed to be a direction-less measure, whereas relatedness is inherently directional. |
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Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
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Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (N18-1)
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| Challenge: | Currently, unsupervised word embeddings are routinely trained on large amounts of raw text data. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
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Embedding Words in Non-Vector Space with Unsupervised Graph Learning (2020.emnlp-main)
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| Challenge: | GraphGlove is an unsupervised graph word representations that are learned end-to-end. |
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