A Deep Learning Study of Aspect Similarity Recognition

Nguyen, Huy-Tien; Vo, Quan-Hoang; Nguyen, Minh-Le

doi:10.1109/kse.2018.8573326

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, potential data set names were recognized using various techniques, including capitalization patterns, associated acronyms, and instances where names are frequently found in proximity to the term "data" or incorporate a concise set of keywords (such as "data set," "survey," etc.). The second model used a machine learning approach that trains a transformer classifier based on whether the string is a data set/survey/report also based on the results of the Kaggle competition (Nguyen & Nguyen, 2021). The model used the Schwartz-Hearst algorithm to identify LONG-NAME (acronym) pattern candidate strings using the pretrained Hugging Face binary classifier and setting up minimum document frequency.…”

Section: Attribution: Finding Nass Data Assets In Nonscientific Publi...mentioning

confidence: 99%

Discovering Data Sets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Pallotta,

M Locklear,

Ren

et al. 2024

Harvard Data Science Review

View full text Add to dashboard Cite

Federal statistical agencies are keenly aware that scientific research is not the only way in which their data assets are used for evidence building. Entities around the world have been building services on top of curated corpuses of scientific research, to help provide insight as to the importance of the works in the collection. This also provides a much-needed framework for statistical agencies and other data set creators to search and find the usages and impacts of those data sets. While the other articles in this special issue largely apply machine learning models to find data sets in an extensively curated corpus, this article starts with a much less structured framework and examines the potential to discover how data sets are used in writing that is targeted at a broader base of users than scientific researchers. It describes the challenges and lessons learned from the exercise; and highlights an ever-growing value proposition for organizing collections of work.

show abstract

Section: Attribution: Finding Nass Data Assets In Nonscientific Publi...mentioning

confidence: 99%

Discovering Data Sets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Pallotta,

M Locklear,

Ren

et al. 2024

Harvard Data Science Review

View full text Add to dashboard Cite

show abstract

“…The sentiment polarity estimation methods were categorised into two machine learning methods and the Lexicon/dictionarybased method in [13]. Lexicon-based methods use sentiment lexicons, which contain a list of sentiment words to determine a given sentiment's rating [14][15][16]. This approach solves previous machine learning problems because there is no need for training data.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Model for Aspect Sentiment Detection

2023

View full text Add to dashboard Cite

Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model.

show abstract

“…A few works utilize these semantic similarity measures to extract aspect categories in one phase; however, all works performed word-level similarity measurement. Recent works find the semantic similarity of a pair of text [12], [13] at the sentence level. But there is a lack of investigation addressing the effect of semantic similarity measures for the aspect category detection task at sentence level in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Similarity Score Model for Aspect Category Detection

Madhoushi¹,

Hamdan²,

Zainudin³

2021

IJACSA

View full text Add to dashboard Cite

Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect. Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models. A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well. This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model. The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner. The datasets of this study, S1 and S2, are from standard SemEval online competition. The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection. This study finds more relevant aspect categories by creating a more stable and robust model. The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset. In dataset S2, the F1score is 72.65% in the laptop domain and 75.11% in the restaurant domain.

show abstract

A Deep Learning Study of Aspect Similarity Recognition

Cited by 4 publications

References 15 publications

Discovering Data Sets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Discovering Data Sets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Semi-Supervised Model for Aspect Sentiment Detection

A Similarity Score Model for Aspect Category Detection

Contact Info

Product

Resources

About