{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dependency-sensitive-convolutional-neural","title":"Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents","arxiv_id":"1611.02361","date":"2016-11-08","proceeding":"NAACL 2016 6","authors":["Rui Zhang","Honglak Lee","Dragomir Radev"],"abstract":"The goal of sentence and document modeling is to accurately represent the\nmeaning of sentences and documents for various Natural Language Processing\ntasks. In this work, we present Dependency Sensitive Convolutional Neural\nNetworks (DSCNN) as a general-purpose classification system for both sentences\nand documents. DSCNN hierarchically builds textual representations by\nprocessing pretrained word embeddings via Long Short-Term Memory networks and\nsubsequently extracting features with convolution operators. Compared with\nexisting recursive neural models with tree structures, DSCNN does not rely on\nparsers and expensive phrase labeling, and thus is not restricted to\nsentence-level tasks. Moreover, unlike other CNN-based models that analyze\nsentences locally by sliding windows, our system captures both the dependency\ninformation within each sentence and relationships across sentences in the same\ndocument. Experiment results demonstrate that our approach is achieving\nstate-of-the-art performance on several tasks, including sentiment analysis,\nquestion type classification, and subjectivity classification.","url_abs":"http://arxiv.org/abs/1611.02361v1","url_pdf":"http://arxiv.org/pdf/1611.02361v1.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":"dependency-sensitive-convolutional-neural","repo_url":"https://github.com/ManuelVs/NNForTextClassification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dependency-sensitive-convolutional-neural","repo_url":"https://github.com/ManuelVs/NeuralNetworks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dependency-sensitive-convolutional-neural","repo_url":"https://github.com/MindSpore-paper-code-2/code399/tree/main/dscnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}