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Unlike other\nhighly sophisticated supervised deep learning models, this paper proposes a\nnovel and yet simple CNN model employing two types of pre-trained embeddings\nfor aspect extraction: general-purpose embeddings and domain-specific\nembeddings. Without using any additional supervision, this model achieves\nsurprisingly good results, outperforming state-of-the-art sophisticated\nexisting methods. To our knowledge, this paper is the first to report such\ndouble embeddings based CNN model for aspect extraction and achieve very good\nresults.","url_abs":"http://arxiv.org/abs/1805.04601v1","url_pdf":"http://arxiv.org/pdf/1805.04601v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"double-embeddings-and-cnn-based-sequence","repo_url":"https://github.com/madehong/Seq2Seq4ATE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"double-embeddings-and-cnn-based-sequence","repo_url":"https://github.com/howardhsu/DE-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"aspect-extraction","task_name":"Aspect Extraction"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-extraction-on-semeval-2014-task-4-sub-2","task":"Aspect Extraction","dataset":"SemEval 2014 Task 4 Sub Task 1","model":"DE-CNN","rank_in_archive_order":2,"of":2,"metrics":{"Laptop (F1)":"81.59"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-extraction-on-semeval-2015-task-12-1","task":"Aspect Extraction","dataset":"SemEval 2015 Task 12","model":"DE-CNN","rank_in_archive_order":1,"of":1,"metrics":{"Restaurant (F1)":"68.28"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-extraction-on-semeval-2016-task-5-sub","task":"Aspect Extraction","dataset":"SemEval 2016 Task 5 Sub Task 1 Slot 2","model":"DE-CNN","rank_in_archive_order":1,"of":1,"metrics":{"Restaurant (F1)":"74.37"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-extraction-on-semeval-2014-task-4-sub-1","task":"Aspect Extraction","dataset":"SemEval-2014 Task-4","model":"DE-CNN","rank_in_archive_order":6,"of":6,"metrics":{"Restaurant (F1)":"85.20"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.04601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04601"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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