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. |
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Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (P19-1)
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| Challenge: | Word embedding models typically learn two types of vectors: target word vectors and context word vector. |
| Approach: | They propose to explicitly impose a cluster structure on context word vectors to improve word embedding models. |
| Outcome: | The proposed model improves word embedding models qualitatively by imposing a cluster structure on the set of context word vectors. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
| Outcome: | The proposed methods produce document-level representations from sentences in 8 languages . the results show that a clever combination of sentence embeddings is usually better than encoding the full document as a single unit. |
Static Word Embeddings for Sentence Semantic Representation (2025.emnlp-main)
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| Challenge: | Existing methods to learn fixed-length embeddings for sentence semantics require large computational cost, making it difficult to process billions of sentences cost-efficiently or deploy models on resource-constrained devices such as smartphones. |
| Approach: | They propose to extract word embeddings from a pre-trained Sentence Transformer and improve them with sentence-level principal component analysis followed by knowledge distillation or contrastive learning. |
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Segmentation-free compositional n-gram embedding (N19-1)
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| Challenge: | Existing word embedding models depend on word segmentation, but this method is difficult when corpora written in noisy or unsegmented languages. |
| Approach: | They propose a new method that models words, phrases and sentences seamlessly without word segmentation. |
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A Document Descriptor using Covariance of Word Vectors (P18-2)
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| Challenge: | Existing methods for retrieving documents using vectors have been used to model documents and queries using bag-of-words (BOW) representations. |
| Approach: | They propose to use the word embeddings of a document to define a novel document descriptor. |
| Outcome: | The proposed descriptor performs well against state-of-the-art methods in supervised and unsupervised environments. |
Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
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 . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
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. |
| Approach: | They propose to embed words in vector space using propositional logic instead of dense vectors . they evaluate embeddings on intrinsic and extrinsic benchmarks and visualize word clusters based on their results . |
| Outcome: | The proposed model outperforms GLoVe on six classification tasks. |
Generalizing Word Embeddings using Bag of Subwords (D18-1)
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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. |
| Approach: | They propose a subword-level word vector generation model that views words as bags of character n-grams and provides good vectors for rare or unseen words. |
| Outcome: | The proposed model performs state-of-the-art in English word similarity task and in joint prediction of part-of speech tag and morphosyntactic attributes in 23 languages. |
Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings (N18-2)
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| Challenge: | Existing methods for producing word embeddings have shown to produce accurate meta-embeddings from pre-trained source embeddables. |
| Approach: | They propose to use arithmetic mean of two distinct word embedding sets to produce an accurate meta-embedding. |
| Outcome: | The proposed method produces meta-embeddings comparable or better than more complex methods. |