SEML: A Semi-Supervised Multi-Task Learning Framework for Aspect-Based Sentiment Analysis
نویسندگان
چکیده
منابع مشابه
IITP: Supervised Machine Learning for Aspect based Sentiment Analysis
The shared task on Aspect based Sentiment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this paper we report our works on aspect term extraction and sentiment classification with respect to our participation in the SemEval-2014 shared task. The aspect term extraction method is based on supervised lea...
متن کاملSupervised Methods for Aspect-Based Sentiment Analysis
In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, implemented and evaluated. We focus on determining the aspect terms existing in each sentence, finding out their polarities, detecting the categories of the sentence and the polarity of each category. The evaluation resul...
متن کاملSemi-Supervised Learning For Sentiment Analysis
We leverage vector space embeddings of sentences and nearest-neighbor methods to transform a small amount of labelled training data into a significantly larger training set using an unlabelled corpus. The quality of the larger training set is measured by prediction accuracy on a benchmark sentiment analysis task. Our results indicate it is possible to achieve accuracy within 3-5% of the baselin...
متن کاملDeep Learning for Aspect-Based Sentiment Analysis
Sentiment analysis is an important task in natural language understanding and has a wide range of real-world applications. The typical sentiment analysis focus on predicting the positive or negative polarity of the given sentence(s). This task works in the setting that the given text has only one aspect and polarity. A more general and complicated task would be to predict the aspects mentioned ...
متن کاملA Multi-kernel Framework for Inductive Semi-supervised Learning
We investigate the benefit of combining both cluster assumption and manifold assumption underlying most of the semi-supervised algorithms using the flexibility and the efficiency of multi-kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming. Promising results o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3031665