| Challenge: | Word embeddings are usually derived from corpora containing text from many individuals . however, they cannot account for user-specific word preferences, such as using the same word in different ways across contexts. |
| Approach: | They propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user. |
| Outcome: | The proposed representations outperform generic representations on two English language tasks. |
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| 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. |
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
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| 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. |
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources (C18-1)
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| Challenge: | Distributional word representations are omnipresent in modern NLP. |
| Approach: | They propose to combine lemmatization and part of speech (POS) typing to improve word embedding performance. |
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Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
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Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
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Embedding Words as Distributions with a Bayesian Skip-gram Model (C18-1)
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| Challenge: | Rather than assuming that word embeddings are fixed across the entire text collection, we generate them from word-specific prior densities for each word. |
| Approach: | They propose a method for embedding words as probability densities in a low-dimensional space from a word-specific prior density for each occurrence of a given word. |
| Outcome: | The proposed method can encode word as a distribution on a range of benchmarks and is comparable to Gaussian embeddings. |
Dynamic Contextualized Word Embeddings (2021.acl-long)
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| 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. |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
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| Challenge: | Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing. |
| Approach: | They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components. |
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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. |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020.acl-main)
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| 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 . |