| { | |
| "paper_id": "O18-1016", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:09:45.499778Z" | |
| }, | |
| "title": "Hierarchical Multi-Label Chinese Word Semantic Labeling using Deep Neural Network", | |
| "authors": [ | |
| { | |
| "first": "Wei-Chieh", | |
| "middle": [], | |
| "last": "\u5468\u744b\u5091", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Chiao Tung University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Chou", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Chiao Tung University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yih-Ru", | |
| "middle": [], | |
| "last": "\u738b\u9038\u5982", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Chiao Tung University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Chiao Tung University", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Traditionally, classifying over 100 hierarchical multi-labels could use flatten classification, but it will lose the taxonomy structure information. This paper aimed to classify the concept of word in E-HowNet and proposed a deep neural network training method with hierarchical relationship in E-HowNet taxonomy. The input of neural network is word embedding. About word embedding, this paper proposed order-aware 2-Bag Word2Vec. Experiment results shown hierarchical classification will achieved higher accuracy than flatten classification.", | |
| "pdf_parse": { | |
| "paper_id": "O18-1016", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Traditionally, classifying over 100 hierarchical multi-labels could use flatten classification, but it will lose the taxonomy structure information. This paper aimed to classify the concept of word in E-HowNet and proposed a deep neural network training method with hierarchical relationship in E-HowNet taxonomy. The input of neural network is word embedding. About word embedding, this paper proposed order-aware 2-Bag Word2Vec. Experiment results shown hierarchical classification will achieved higher accuracy than flatten classification.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "The 2018 Conference on Computational Linguistics and Speech Processing ROCLING 2018, pp. 157-157 \u00a9The Association for Computational Linguistics and Chinese Language Processing", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": {}, | |
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| } | |
| } |