Sentiment Classification Using Document Embeddings Trained with Cosine Similarity (P19-2)
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| Challenge: | Existing document embedding models map each document to a dense, low-dimensional vector in continuous vector space. |
| Approach: | They propose to train document embeddings using cosine similarity instead of dot product . they focus on document sentiment classification of long movie reviews . |
| Outcome: | The proposed model improves document embedding accuracy by using cosine similarity instead of dot product on the IMDB dataset while using feature combination with Naive Bayes weighted bag of n-grams achieves 93.68% accuracy. |
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Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets (N18-4)
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| Challenge: | Existing word embeddings for sentiment analysis are limited in domain specific applications . generic word embeds are poor initialization for tasks on domain specific data sets. |
| Approach: | They propose to use word embeddings adapted for domain specific data sets in sentiment classification applications. |
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Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains. (C18-1)
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| Challenge: | Standard word embedding algorithms learn vector representations from large corpora of text documents in unsupervised fashion. |
| Approach: | They propose an algorithm that learns word embeddings jointly with a classifier . their algorithm leverages document label information to learn vector representations of words . |
| Outcome: | The proposed algorithm has superior performance on domains with limited data compared to other methods. |
A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
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Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)
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| 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. |
Domain Adapted Word Embeddings for Improved Sentiment Classification (P18-2)
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| Challenge: | Generic word embeddings are trained on large-scale generic corpora, while domain specific ones are trained only on data from a domain of interest. |
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Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing (N19-1)
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| Challenge: | Existing methods to estimate document similarity based on word embeddings are mediocre . a recent study compared word and sentence embedded documents to a similarity estimate using matrix norms. |
| Approach: | They propose to combine word embeddings with matrix norms to obtain a similarity estimate. |
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A Document Descriptor using Covariance of Word Vectors (P18-2)
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| Challenge: | Existing methods for retrieving documents using vectors have been used to model documents and queries using bag-of-words (BOW) representations. |
| Approach: | They propose to use the word embeddings of a document to define a novel document descriptor. |
| Outcome: | The proposed descriptor performs well against state-of-the-art methods in supervised and unsupervised environments. |
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation (N19-1)
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| Challenge: | a novel word embedding representation for text documents is proposed . the method is based on the Vector of Locally-Aggregated Descriptors used for image representation . |
| Approach: | They propose a novel representation for text documents based on aggregating word embedding vectors into document embeddables. |
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Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Joint Embedding of Words and Labels for Text Classification (P18-1)
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Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
| Challenge: | Existing approaches to text classification use word embeddings to capture semantic regularities between words. |
| Approach: | They propose to view text classification as a label-word joint embedding problem . they use a framework that measures compatibility between text sequences and labels . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on large text datasets. |