This paper classifies tweets into positive and negative sentiments using distributed word and sentence representations.
problem Classifying tweets into positive and negative sentiments.
method Used distributed representations of words and sentences, and LSTM and CNN networks for classification.
result Achieved accuracies as high as 81%.
Survey of word embedding techniques for NLP.
problem Creating effective word representations for natural language processing.
method Describes recent strategies for fixed-length, dense word embeddings.
result Word embeddings encode syntactic and semantic information and improve NLP tasks.
Tile2Vec learns spatially meaningful representations without labels.
problem Lack of unsupervised methods for geospatial data.
method Unsupervised representation learning using the distributional hypothesis.
result Tile2Vec improves performance in spatial classification tasks.
Approaches KL divergence for learning multi-sense word distributions.
problem Capturing the polysemy and uncertainty of words in word embeddings.
method Modeling words as multi-sense Gaussian mixtures and using KL divergence for learning.
result The proposed approach effectively captures word entailment and distribution similarity.
Trains word embeddings from music and text data to link music contexts.
problem Varying vocabulary size and musical relevance in word embeddings.
method Combines general text and music-specific data to train word embeddings.
result Trained embeddings better associate music contexts with compositions.
Top2Vec finds topic vectors from documents and words without needing stop words or custom settings.
problem Topic modeling weaknesses, including needing known topics, stop words, and custom settings.
method Joint document and word semantic embedding to find topic vectors automatically.
result Top2Vec finds more informative and representative topics than probabilistic models.
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the traini…
New word distributions capture multiple meanings and outperform existing methods.
problem Capturing semantic information for words with multiple meanings.
method Gaussian mixtures with an energy-based max-margin objective.
result Multimodal word distributions outperform word2vec and Gaussian embeddings.
New methods for unsupervised learning of word and entity representations.
problem Learning distributed representations of words and entities from text and knowledge bases.
method MVLSA for words and NVSE for entities, both unsupervised learning methods.
result MVLSA and NVSE outperform state-of-the-art models in word and entity representation learning.
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
Paper proposes a method for cross-lingual sentiment classification using distributed word representations.
problem Cross-lingual sentiment classification with strict one-to-one word mapping limitations.
method Uses distributed word representations to learn meaningful one-to-many mappings for pivot words.
result Method outperforms state-of-the-art in cross-lingual sentiment classification.
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual dat…
Model learns multilingual word representations robust to noise.
problem Learning multilingual word representations in noisy environments.
method Fit a generative latent variable model to a multilingual dictionary.
result Competitive multilingual embeddings across various tasks.
Hierarchical density embeddings capture word relationships with uncertainty.
problem Capturing semantic relationships and uncertainty in word embeddings.
method Learn hierarchical representations through probability density encapsulation, using simple loss functions and distance metrics.
result State-of-the-art performance on WordNet and Hyperlex datasets.
Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropria…
CPGBN analyzes text sequences, capturing word order for better topic extraction.
problem Lack of word order in traditional text representations.
method CPFA processes words as sequences, CPGBN adds hierarchical topic modeling.
result CPGBN extracts high-quality latent representations capturing word order.
Unsupervised model learns word and context embeddings from character sequences.
problem Learning meaningful word and context embeddings from unlabeled data.
method Character-aware neural architecture that jointly learns word and context embeddings.
result Compact encoders achieve high performance in downstream tasks.
Enhances topic models to better handle polysemous words.
problem Lack of polysemy handling in Gaussian latent Dirichlet allocation.
method Introduces a hierarchical structure to capture polysemy in Gaussian latent Dirichlet allocation.
result Significantly improves polysemy detection and provides more parsimonious topic representations.
Bayesian algorithm improves word representations using semantic taxonomy.
problem Improving word representations in semantic taxonomy.
method Bayesian Hierarchical Words Representation (BHWR) learning algorithm combining Variational Bayes and semantic taxonomy modeling.
result BHWR produces better representations for rare words.
Estimator Vectors learns OOV word embeddings using subword and context clues.
problem Lack of OOV word representations in neural network models.
method Jointly learns word, subword, and context clue representations.
result Strong estimates for OOV words via combined subword and context clue embeddings.
The study examines how character and word-level representations improve sentence-level tasks.
problem Improving the quality of word and sentence representations using character-level information.
method Feature-wise sigmoid gating mechanism for combining character and word-level representations.
result Modeling characters improves final word and sentence representations, especially for less frequent words.
Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multil…
Improves text clustering by incorporating sequential features and word embeddings.
problem Lack of sequential information and synonym handling in current text clustering methods.
method SiDPMM model that models documents as joint of bags of words, sequential features, and word embeddings.
result Significant improvement in performance and accurate inference of cluster numbers.
GROVER improves word representations by gradually adding random noises during training.
problem Improving word representations for better model performance.
method Gradually adding random noises to word embeddings during training.
result GROVER improves model performances on most text classification datasets.
PCA whitening weighted by Zipfian word frequencies improves task performance.
problem Skewed word embedding spaces in neural models.
method PCA whitening weighted by empirical word frequencies following Zipf's law.
result Significantly improves task performance, surpassing baselines.
This paper proposes using contextualized word representations for better concept taxonomies.
problem Current taxonomy learning systems define concepts as single words, limiting their semantic understanding.
method Defines concepts as synsets, learns density-based approximations of contextualized word representations, and measures similarity and hypernymy.
result Contextualized word representations can improve the accuracy of concept taxonomies.
Paper finds better words for topic models by reranking top words.
problem Top words in topic models are not always representative.
method Reranking words by considering marginal probability over every topic.
result Reranked top words are more representative of topics.
Simpler postprocessing boosts word representations.
problem Improving word representations for NLP tasks.
method Eliminating mean vector and top directions from word vectors.
result Processed representations outperform original ones across various tasks.
This paper proposes an efficient method to train word embeddings for large corpora without synchronization.
problem Training word embeddings for large text corpora is computationally expensive and requires synchronization.
method Partition the input space instead of the vocabulary size, using asynchronous training without parameter synchronization.
result Comparable and up to 45% performance improvement in NLP benchmarks with 1/10 the training time.
The abstract explains how word and relation representations capture semantic meaning.
problem Understanding how word and relation representations capture semantic meaning.
method Theoretical justification and extension of geometric relationships between word embeddings and knowledge graph representations.
result The geometric relationships between word embeddings correspond to semantic relations between words and entities in knowledge graphs.
A new neural topic model using optimal transport improves document representation and topic coherence.
problem Challenges in achieving good document representation and coherent/diverse topics in existing NTMs.
method Proposes a neural topic model via optimal transport, learning topic distribution by minimising OT distance to document word distributions.
result Significantly outperforms state-of-the-art NTMs on discovering coherent and diverse topics.
Researchers solve word2vec's Corpus Replication Task to understand relational word similarities.
problem Understanding how word2vec captures relational similarities in word embeddings.
method Propose a Corpus Replication Task to generate input text for word2vec that outputs specific target relations.
result Demonstrates that word2vec can capture relational similarities in word embeddings.
This paper assesses biases in contextualized word representations.
problem Analyzing biases in contextualized word representations.
method Proposes assessing bias at the contextual word level, capturing contextual effects of bias.
result Demonstrates evidence of bias in contextual word models, including racial bias and exacerbated effects for intersectional minorities.
The paper improves part-of-speech tagging with multi-task learning and character-level word representations.
problem Improving part-of-speech tagging accuracy.
method Developed a new character-level word representation using feedforward neural network, pretraining with existing word vectors, and an additional prediction of neighbour labels as an auxiliary loss.
result The methods significantly improved POS tagging performance on English and Russian languages.
This work compares meta-embeddings for natural language processing tasks.
problem Improving distributed word representations in natural language processing.
method Meta-embeddings trained with angular and normalized distance loss functions compared to standard loss functions.
result Normalization methods outperform standard loss functions on word similarity datasets.
Probabilistic FastText captures multiple word senses and sub-word structures.
problem Capturing multiple word senses and sub-word structures in word embeddings.
method Probabilistic FastText uses Gaussian mixture densities to represent words, sharing statistical strength across sub-word structures and capturing different word senses.
result Probabilistic FastText outperforms existing models on word-similarity benchmarks and discerning different meanings.
EigenNoise provides a competitive word vector initialization scheme without pre-training data.
problem Improving word vector initialization without pre-training data.
method EigenNoise uses a dense, independent co-occurrence model to initialize word vectors.
result EigenNoise can approach GloVe performance without pre-training data.
Improved SGNS model resolves ambiguity in word vector learning.
problem Ambiguity in word vectors under SGNS model.
method Rectified SGNS model with quadratic regularization.
result Simple modification structures the solution effectively.
We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor…
Proposes methods to model polysemous words using vector representations and geometric analysis.
problem Modeling words with multiple meanings in NLP.
method Three-fold approach: context representations, sense induction, lexeme representations.
result Disambiguation of word senses using low-rank subspaces and Grassmannian geometry.
Equation embeddings learn from surrounding words to represent math equations.
problem Math equations are hard to analyze due to their uniqueness.
method Equation embeddings use surrounding words to find good representations of equations.
result Equation embeddings provide better models than existing word embedding approaches.
Fruit fly brain network learns word embeddings using sparse binary codes.
problem Learning semantic word representations from text.
method Inspired by mushroom body neural network, sparse binary hash codes.
result Fruit fly network achieves comparable NLP performance with reduced resources.
SWESA learns word embeddings with document labels for sentiment analysis.
problem Sentiment analysis using limited text data.
method SWESA uses supervised learning to optimize word embeddings and classifier performance.
result SWESA outperforms existing methods in sentiment analysis.
A new text representation model combines CNN and VAE for better semantic extraction.
problem Difficult to effectively extract semantic features and distinguish polysemy in text data.
method Integrates CNN for feature extraction and VAE for consistent Gaussian distribution.
result The model outperforms traditional classification algorithms in text classification tasks.
D-ETM models document topics over time using embeddings and variational inference.
problem Capturing evolving topic patterns in sequential documents.
method Combines D-LDA and word embeddings, using random walk priors and variational inference.
result D-ETM outperforms D-LDA on document completion tasks, learning more diverse and coherent topics.
Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a rank…
Enhances image captioning with novel context combination methods.
problem Improving machine learning for image captioning with structured learning and meaningful interpretation.
method Combines Feature Distribution Composition (FDC), Multiple Role Representation Crossover (MRRC) attention layers, and language decoder.
result Significantly improved image captioning performance (35.3%) and established new standards.
KATE improves text autoencoders by learning meaningful representations.
problem Text autoencoders struggle with learning meaningful representations due to confounding properties.
method Proposes KATE, a k-competitive autoencoder that uses competition to specialize neurons in recognizing specific patterns.
result KATE learns better representations than traditional autoencoders and deep generative models.