Abstract:Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three… Show more
“…Word Representation uses Word Embedding which is described in III while the Indonesian Word Embedding is 100 vector sizes made with GloVe with the entire book of Mark dataset. Character Embedding [31] uses 3 types of filters with sizes [3,4,5] and each filter has 50, while the initial representation of the characters themselves has dimensions of 8.…”
Section: Methodsmentioning
confidence: 99%
“…Coreference Resolution is a process of determining if 2 or more expressions in natural language are the same entity in the real world [3]. Coreference Resolution is also applied to assist other natural language processing tasks, namely Question Answering [4], Sentiment Analysis [5], Document Summarization, Quote Attribution, and Information Extraction.…”
This research evaluates the performance of End-to-End Neural Coreference Resolution models in English and Indonesian linguistic contexts, drawing particular attention to the model by K. Lee, recognized for its simplified preprocessing methodology. In this research, we use raw text to find and link mentions in documents, and handle different languages. For the evaluation metrics, the English model achieved F1-Scores of 67.94% and 67.14% on the OntoNotes-5.0 development and training sets. The Indonesian model, prepared using a CoNLL-2012 formatted dataset, attained an F1-Score of 68.88% on a 25% segment of the Book of Markus. We also analyzed the additional features integrated into the model to assess their contributions to performance improvement. The findings indicate that while the English model demonstrates generalizability across various coreference challenges, the Indonesian model's performance is more domain-specific, being particularly effective within the confines of the Book of Mark.
“…Word Representation uses Word Embedding which is described in III while the Indonesian Word Embedding is 100 vector sizes made with GloVe with the entire book of Mark dataset. Character Embedding [31] uses 3 types of filters with sizes [3,4,5] and each filter has 50, while the initial representation of the characters themselves has dimensions of 8.…”
Section: Methodsmentioning
confidence: 99%
“…Coreference Resolution is a process of determining if 2 or more expressions in natural language are the same entity in the real world [3]. Coreference Resolution is also applied to assist other natural language processing tasks, namely Question Answering [4], Sentiment Analysis [5], Document Summarization, Quote Attribution, and Information Extraction.…”
This research evaluates the performance of End-to-End Neural Coreference Resolution models in English and Indonesian linguistic contexts, drawing particular attention to the model by K. Lee, recognized for its simplified preprocessing methodology. In this research, we use raw text to find and link mentions in documents, and handle different languages. For the evaluation metrics, the English model achieved F1-Scores of 67.94% and 67.14% on the OntoNotes-5.0 development and training sets. The Indonesian model, prepared using a CoNLL-2012 formatted dataset, attained an F1-Score of 68.88% on a 25% segment of the Book of Markus. We also analyzed the additional features integrated into the model to assess their contributions to performance improvement. The findings indicate that while the English model demonstrates generalizability across various coreference challenges, the Indonesian model's performance is more domain-specific, being particularly effective within the confines of the Book of Mark.
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