| Challenge: | Using word embeddings, we analyze spelling variation in word embeds trained on Twitter and Reddit data. |
| Approach: | They propose a new perspective on the analysis of word embeddings by focusing on spelling variation. |
| Outcome: | The proposed analysis shows that word embeddings encode spelling variation patterns of various types to some extent, even when trained using the skipgram model. |
Similar Papers
Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
Copied to clipboard
| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
| Outcome: | The proposed results provide insights into word embedding stability in English and other languages. |
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation (N18-4)
Copied to clipboard
| Challenge: | Using extrinsic evaluation methods, embeddings are evaluated on a specific task such as part-of-speech tagging or named-entity recognition. |
| Approach: | They propose a method to study the variation between word embeddings models trained with only one parameter by observing the distributional neighbors variation. |
| Outcome: | The proposed method shows that changing only one parameter can have a massive impact on a given semantic space. |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020.acl-main)
Copied to clipboard
| Challenge: | a study of word embeddings shows that social biases are more accurate than survey data for some dimensions of meaning. |
| Approach: | a new study investigates the extent to which word embeddings accurately reflect biases . they find that biased word embeds mirror survey data across 17 dimensions of social meaning . |
| Outcome: | a new study shows that word embeddings accurately reflect biases on average across dimensions of social meaning . biased embedders are more reflective of survey data for some dimensions of meaning than others, the study finds . |
Exploring the Value of Personalized Word Embeddings (2020.coling-main)
Copied to clipboard
| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
| Approach: | They propose personalized word embeddings and compare their performance to generic ones . they show that personalized word representations can be leveraged for improved performance . |
| Outcome: | The proposed model can be used for authorship attribution. |
Querying Word Embeddings for Similarity and Relatedness (N18-1)
Copied to clipboard
| 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. |
| Approach: | They propose to use word embeddings to predict asymmetric association between words from a dataset of production norms to generate thematically related words. |
| Outcome: | The proposed model predicts asymmetric association between words from a recently published dataset of production norms. |
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing studies have attempted to personalize models to improve performance on NLP tasks such as sentiment analysis but they did not estimate subjective input. |
| Approach: | They propose a method of modeling personal biases in word meanings with personalized word embeddings by solving a task on subjective text while regarding words used by different individuals as different words. |
| Outcome: | The proposed method improves sentiment analysis and target task with reviews retrieved from RateBeer. |
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent research has shown that static word embeddings can encode words’ frequencies, but little has been studied about this behavior. |
| Approach: | They propose to use static word embeddings to encode words' frequencies and to assess the impact of this relationship on embeddable bias metrics. |
| Outcome: | The proposed model shows that word embeddings can produce higher similarity between high-frequency words than other embeddables. |
Dynamic Contextualized Word Embeddings (2021.acl-long)
Copied to clipboard
| Challenge: | Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts. |
| Approach: | They propose dynamic contextualized word embeddings that represent words as a function of linguistic and extralinguistic contexts. |
| Outcome: | The proposed model models time and social space jointly, making them attractive for NLP tasks involving semantic variability. |
Addressing Noise in Multidialectal Word Embeddings (P18-2)
Copied to clipboard
| Challenge: | Dialectal Arabic (DA) is problematically noisy and lacks a large corpus of non-noisy words. |
| Approach: | They propose to use word embedding tools to maximize the informative content leveraged in each training sentence and analyze methods for representing disparate dialects in one embeddable space. |
| Outcome: | The proposed methods improve performance on low and high frequency words while preserving accuracy on low frequency forms. |
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings (P19-1)
Copied to clipboard
| Challenge: | Empirical analysis of word embeddings of ambiguous words is limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. |
| Approach: | They present a large dataset based on manual Wikipedia annotations and word senses, where word sense from different words are related by semantic classes. |
| Outcome: | The proposed method can predict whether a word is single-sense or multi-sensor, if the sense is frequent, and it can predict rare senses. |