| Challenge: | Word embeddings that encode lexical-semantic relations do not capture emotion aspects of words. |
| Approach: | They propose a retrofitting method to update the vectors of emotion bearing words . they find that the retrofitted embeddings achieve better distances between clusters . |
| Outcome: | The proposed method achieves better distances between clusters and clusters for words having the same emotions. |
Similar Papers
Affective Retrofitted Word Embeddings (2022.aacl-main)
Copied to clipboard
| Challenge: | Word embeddings do not capture affective dimensions of valence, arousal, and dominance . valency, valance, and adolescence are present in words, but are not represented in text . |
| Approach: | They propose a method for updating word embeddings for affective meaning . they use a non-linear transformation function that maps pre-trained embedders to an affective vector space . |
| Outcome: | The proposed method improves inter-cluster and intra-c cluster distances for emotion-bearing words. |
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | a novel retrofitting method to induce emotion aspects into pre-trained language models is proposed . the models are computationally less expensive and open, but do not capture affective aspects of human communication well. |
| Approach: | They propose a retrofitting method to induce emotion aspects into pre-trained language models . they retrofit text fragments exhibiting similar emotions into pretrained networks . |
| Outcome: | The proposed method produces emotion-aware text representations for sentiment analysis and sarcasm detection tasks. |
Learning Emotion-enriched Word Representations (C18-1)
Copied to clipboard
| Challenge: | Existing word representations based on distributional hypothesis do not provide accurate representations of emotions. |
| Approach: | They propose a method to obtain emotion-enriched word representations by remote supervision using a large training dataset of text documents and two recurrent neural network architectures. |
| Outcome: | The proposed method outperforms competing general-purpose and affective representations on two tasks. |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
Copied to clipboard
| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
Copied to clipboard
| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
Explicit Retrofitting of Distributional Word Vectors (P18-1)
Copied to clipboard
| Challenge: | Existing models for word vector specialization focus on word co-occurrences from large text corpora, resulting in a tendency to fuse semantic similarity with other types of semantic relatedness. |
| Approach: | They propose to transform external lexico-semantic relations into training examples and learn an explicit retrofitting model. |
| Outcome: | The proposed model can specialize vector spaces of new languages and translate them to other languages. |
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings (2020.coling-main)
Copied to clipboard
| Challenge: | Existing word embedding models mix semantic similarity with other types of relatedness. |
| Approach: | They propose a model that leverages relational knowledge available in a knowledge resource to improve word embeddings. |
| Outcome: | The proposed model improves word embeddings on synonymy, antonymy and hypernymy relations in WordNet and significantly improves lexical entailment detection task. |
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)
Copied to clipboard
| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
| Approach: | They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels. |
| Outcome: | The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media . |
What just happened? Evaluating retrofitted distributional word vectors (N19-1)
Copied to clipboard
| Challenge: | Recent work has attempted to enhance vector space representations using information from structured semantic resources. |
| Approach: | They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting. |
| Outcome: | The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric. |
Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)
Copied to clipboard
| Challenge: | Existing methods for embedding sentiment knowledge into word vectors are generally trained independently of the downstream task. |
| Approach: | They propose to encode sentiment knowledge into pre-trained word vectors to improve sentiment analysis. |
| Outcome: | The proposed method improves sentiment analysis on four popular sentiment datasets compared to benchmark methods. |