New algorithms improve automated text sentiment analysis.
problem Automated classification of text sentiment.
method Two new Genetic Algorithms (GAs) for identifying sentiment and amplifier words in text.
result Our approach outperformed existing algorithms in sentiment analysis experiments.
Word embeddings trained on Google News articles show gender biases.
problem Gender stereotypes in word embeddings amplify biases in machine learning tasks.
method Identify and modify the geometric direction capturing gender bias, using linear separability and algorithms to remove bias while preserving useful properties.
result Our debiasing algorithms significantly reduce gender bias in word embeddings.
New method debiases word embeddings for multiclass settings like race and religion.
problem Word embeddings in online texts perpetuate human stereotypes, including race and religion.
method Proposes a novel methodology to debias word embeddings in multiclass settings.
result Demonstrates robust multiclass debiasing that maintains NLP task efficacy.
New method to understand bias in word embeddings.
problem Understanding and mitigating bias in word embeddings.
method Developed a technique to trace bias origins back to training documents.
result Accurate approximations of bias reduction can be made.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
Paper studies and reduces gender stereotypes in word embeddings.
problem Word embeddings can reflect societal stereotypes, affecting text data representation.
method Created a gender analogy task, used crowdsourcing, developed an efficient algorithm.
result Successfully reduced gender stereotypes in word embeddings using a few training examples.
Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This pr…
Production networks amplify economic growth through technology diffusion.
problem Understanding how technology improvements propagate through production networks.
method Analyzing a production network model to study the effects of technological improvements.
result Longer production chains lead to faster price reduction and GDP growth.
Active learning improves EDFA model accuracy with binary features.
problem Lack of labeled training data for EDFA devices.
method Active learning strategy for binary features using sparse linear models.
result Improved prediction and accelerated query generation.
Most of the econometric and econophysics models have been borrowed from the statistical physics, and as a cosequence, a new interdisciplinary science called econophysics has emerged. In this paper we planned to extend the analogy between different economic processes or phenomena and processes and phenomena from differe…
The paper shows how to amplify small datasets to look like they came from a known distribution.
problem How to increase dataset size when learning from an unknown distribution is impossible.
method Develops amplification procedures to output larger sets of samples that mimic the original distribution.
result Valid amplification procedures exist even when the input dataset is significantly smaller than needed for learning.
A new federated learning framework with sparsification and adaptive optimization for privacy and efficiency.
problem Lack of sufficient privacy protection in federated learning.
method Integrates random sparsification with gradient perturbation and acceleration techniques to enhance privacy and efficiency.
result Outperforms previous differentially-private federated learning approaches in privacy and efficiency.
The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wi…
Mitigates gender bias amplification in model predictions.
problem Gender bias amplification in model predictions.
method Posterior regularization to mitigate bias.
result Almost removes gender bias amplification in model predictions.
Paper learns identity-sensitive word embeddings from text corpora.
problem Lack of context-aware word embeddings.
method Constructs a heterogeneous network of words and identities, then embeds into a low-dimensional space.
result Identity-sensitive word embeddings capture different meanings of words.
Generative AI amplifies data without increasing information, with a mathematical limit.
problem Data amplification without information gain.
method Information theoretic concepts applied to GAN-generated data.
result A mathematical bound on gain is established, showing data can be amplified without increasing information content.
A new method selects anchor words for better topic discovery in text corpora.
problem Selecting anchor words for improved topic modeling in text corpora.
method Proposes a new greedy method to find a minimum edge-weight anchor clique in a word similarity graph.
result The proposed method outperforms existing methods on topic quality and is faster.
There are certain families of words and word sequences (words in the generators of a two-generator group) that arise frequently in the Teichm{ü}ller theory of hyperbolic three-manifolds and Kleinian and Fuchsian groups and in the discreteness problem for two generator matrix groups. We survey some of the families of su…
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.
FRAGE learns word embeddings without frequency bias, improving performance across NLP tasks.
problem Word embeddings are biased towards word frequency, affecting performance for rare words.
method Adversarial training to learn Frequency-Agnostic word Embedding (FRAGE).
result FRAGE achieves higher performance than baselines in all four NLP tasks.
Geometrically transforms word embeddings into a common space for better comparison.
problem Comparing embeddings from different sources is challenging.
method Applies orthogonal rotations and Mahalanobis scaling to transform embeddings into a shared latent space.
result The method improves word similarity and analogy tasks.
A transformer model improves spell correction with hierarchical attention.
problem Improving spell correction accuracy and speed.
method Multi encoder-single decoder transformer architecture with hierarchical attention.
result Significant improvement in CER, WER, and SER error rates.
We discuss a topological approach to words introduced by the author. Words on an arbitrary alphabet are approximated by Gauss words and then studied up to natural modifications inspired by the Reidemeister moves on knot diagrams. This leads us to a notion of homotopy for words. We introduce several homotopy invariants …
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.
Paper proposes a new word embedding method optimizing word similarity.
problem Optimizing word similarity in embedding space.
method Two-step random walks between words via topics to learn an optimal embedding simplex.
result Our method outperforms existing approaches in various queries.
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.
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.
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.
Paper proposes MMD-Sense-Analysis for detecting word sense shifts.
problem Detecting and interpreting shifts in word meanings over time.
method Leverages Maximum Mean Discrepancy (MMD) to identify and explain word sense changes.
result Demonstrates effectiveness of MMD-Sense-Analysis through empirical results.
Traded corporations are required by law to have a majority of outside directors on their board. This requirement allows the existence of directors who sit on the board of two or more corporations at the same time, generating what is commonly known as interlocking directorates. While research has shown that networks of …
In spite of the growing theoretical literature on cascades of failures in interbank lending networks, empirical results seem to suggest that networks of direct exposures are not the major channel of financial contagion. In this paper we show that networks of interbank exposures can however significantly amplify contagi…
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.
New method improves ASR word confidence for diverse applications.
problem Mitigating ASR errors and improving word error rate.
method Heterogeneous Word Confusion Network (HWCN) with score calibration.
result Word sequence with best overall confidence is more accurate than 1-best result.
Word2vec improved but lacks multi-meaning words; ConEc creates new embeddings.
problem Lack of meaningful embeddings for words with multiple meanings and OOV words.
method Context encoders (ConEc) extend word2vec by multiplying embeddings with context vectors.
result ConEc creates embeddings for OOV words and words with multiple meanings based on local contexts.
New method uses word subspaces and term-frequency to improve text classification.
problem Lack of semantic meaning in bag-of-words features.
method Proposes word subspaces and term-frequency weighted word subspaces for text classification.
result Improved text classification performance compared to state-of-the-art algorithms.
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…
Proposes MorphMine for unsupervised morpheme segmentation to improve word embeddings.
problem Lack of semantic information in word-level analysis for infrequent and out-of-vocabulary words.
method MorphMine applies a parsimony criterion to hierarchically segment words into the fewest number of morphemes.
result MorphMine segments words into human-verified morphemes and improves word embedding quality.
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.
Word embeddings reveal multiple senses, which can be recovered using sparse coding.
problem Understanding word senses in polysemous words.
method Sparse coding of word embeddings to recover multiple senses.
result Sparse coding can approximate multiple word senses, with each sense associated with a discourse atom.
End-to-end ASR model combines word and character representation for improved performance.
problem Difficulty in training with word-level supervision due to sparsity of examples.
method Multi-task learning framework combining word and character representations.
result Improved word-error rate (WER) by interpolating between word-level and character-level models.
Paper analyzes word embedding composition using tensor decomposition.
problem Given vector representations of two words, compute a vector for the entire phrase.
method Generative model with low rank Tucker decomposition of word embedding correlations.
result Word embeddings and a core tensor can be derived from the Tucker decomposition.
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.
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.
By defining combinatorial moves, we can define an equivalence relation on Gauss words called homotopy. In this paper we define a homotopy invariant of Gauss words. We use this to show that there exist Gauss words that are not homotopically equivalent to the empty Gauss word, disproving a conjecture by Turaev. In fact, …
We introduce a topological approach to words. Words are approximated by Gauss words and then studied up to natural modifications inspired by homotopy transformations of curves on the plane.
A longstanding question of Gromov asks whether every one-ended word-hyperbolic group contains a subgroup isomorphic to the fundamental group of a closed hyperbolic surface. An infinite family of word-hyperbolic groups can be obtained by taking doubles of free groups amalgamated along words that are not proper powers. W…
Word embeddings in hyperbolic space outperform Euclidean ones.
problem Improving word embeddings for better performance.
method Learning word embeddings in hyperbolic space using skip-gram architecture and hyperbolic distance objective function.
result Hyperbolic word embeddings show potential, especially in low dimensions, but not clear superiority over Euclidean embeddings.
Enhanced word embeddings boost multiclass text classification accuracy.
problem Improving multiclass text classification accuracy using pre-trained embeddings.
method Proposed word-class embeddings (WCEs) to enhance pre-trained word embeddings.
result WCEs significantly improve multiclass text classification accuracy.