| { | |
| "paper_id": "O10-4004", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:06:41.749512Z" | |
| }, | |
| "title": "Annotating Phonetic Component of Chinese Characters Using Constrained Optimization and Pronunciation Distribution", | |
| "authors": [ | |
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| { | |
| "first": "Chia-Hui", | |
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| "last": "Chang", | |
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| { | |
| "first": "Shu-Yen", | |
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| { | |
| "first": "Shu-Ying", | |
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| "last": "Li", | |
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| { | |
| "first": "Meng-Feng", | |
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| "last": "Tsai", | |
| "suffix": "", | |
| "affiliation": {}, | |
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| { | |
| "first": "Shu-Ping", | |
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| "last": "Li", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Hsiang-Mei", | |
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| "last": "Liao", | |
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| { | |
| "first": "Chih-Wen", | |
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| { | |
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| "abstract": "Generally speaking, Chinese characters are graphic characters that do not allow immediate pronunciation unless they are accompanied with Mandarin phonetic symbols (zhuyin) or other pinyin methods (e.g. romanization system). In fact, about 80 to 90 percents of Chinese characters are pictophonetic characters which are composed of a phonetic component and a semantic component. Therefore, even if one had not seen the character before, one can make a logical guess at the character's pronunciation and meaning from its phonetic and semantic symbols. In order to analyze such relations, we start by analyzing the characteristics of phonetic components. We found two interesting features that could automatically identify the phonectic components of Chinese characters. One is pronunciation similarity, the other is pronunciation distribution. Experiments show that these two methods have high accuracy (90.8% and 98.1% for 9593 pictophonetic characters) in predicting the phonetic components of pictophonetic characters. These methods can save a lot of time and effort during the annotation of phonetic symbols in the early stage.", | |
| "pdf_parse": { | |
| "paper_id": "O10-4004", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Generally speaking, Chinese characters are graphic characters that do not allow immediate pronunciation unless they are accompanied with Mandarin phonetic symbols (zhuyin) or other pinyin methods (e.g. romanization system). In fact, about 80 to 90 percents of Chinese characters are pictophonetic characters which are composed of a phonetic component and a semantic component. Therefore, even if one had not seen the character before, one can make a logical guess at the character's pronunciation and meaning from its phonetic and semantic symbols. In order to analyze such relations, we start by analyzing the characteristics of phonetic components. We found two interesting features that could automatically identify the phonectic components of Chinese characters. One is pronunciation similarity, the other is pronunciation distribution. Experiments show that these two methods have high accuracy (90.8% and 98.1% for 9593 pictophonetic characters) in predicting the phonetic components of pictophonetic characters. These methods can save a lot of time and effort during the annotation of phonetic symbols in the early stage.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
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| "body_text": [ | |
| { | |
| "text": "EQUATION", | |
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| "text": "EQUATION", | |
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M 1 , , , , ,", | |
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| "text": "EQUATION", | |
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| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "S \u4ee3\u8868\u67d0\u4e9b\u6f22\u5b57\u6240\u5f62\u6210\u7684\u96c6\u5408\uff0c\u0192(S)\u3001g(S)\u3001h(S)\u5206\u8868\u793a\u5176\u8072\u6bcd\u3001\u97fb\u6bcd\u53ca\u8072\u8abf\u7684 \u5206\u4f48\u6a5f\uf961\u3002\uf9a8 A \u8868\u793a\u6240\u6709\u6f22\u5b57\u6240\u6210\u7684\u96c6\u5408\uff0c\u5247\u0192(A)\u3001g(A)\u3001h(A)\u5206\u5225\u8868\u793a\u6f22\u5b57\u7684\u8072\u6bcd\u3001 \u97fb\u6bcd\u53ca\u8072\u8abf\u7684\u5206\u4f48\u6a5f\uf961\u3002\u540c\uf9e4\u5c0d\u65bc\u4e00\u500b\u6f22\u5b57\u69cb\u4ef6 b\uff0c\u6211\u5011\u53ef\u4ee5\u627e\u51fa\u5305\u542b b \u7684\u6240\u6709\u6f22\u5b57 B\uff0c \u540c\u6642\u6c42\u5f97\u5176\u8072\u6bcd\u3001\u97fb\u6bcd\u53ca\u8072\u8abf\u7684\u5206\u4f48\u6a5f\uf961\u0192(B)\u3001g(B)\u3001h(B)\u3002\uf974\u662f b \u767c\u97f3\u96c6\u4e2d\ufa01\u8f03\u9ad8\uff0c\u5247 \u5176\u8072\u6bcd\u5206\u4f48\u0192(B)\u8207\u0192(A)\u5c31\u6703\u6709\u8f03\u5927\u7684\u5dee\uf962\u3002\u56e0\u6b64\u6211\u5011\u63a1\u7528 Kullback-Leibler divergence \u7684 \u65b9\u6cd5\uf92d\u8a08\u7b97\uf978\u500b\u5206\u4f48\u7684\u8ddd\uf9ea\u3002Kullback-Leibler divergence \u7684\u516c\u5f0f\u5982\u4e0b: i P(i) (P||Q) P(i)log Q(i) KL = \u2211", | |
| "eq_num": "(4" | |
| } | |
| ], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u542b\u69cb\u4ef6\u5305 |C|=32 0 0 0 0 0 0 0 0 0 0 f(C) 0 0 0 0 0 0 0 0 0 0 \u5c07|A|\u8207|B|\u6b63\u898f\u5316\u5f97 f(A)\u8207 f(B)\uf978\u6a5f\uf961\u5206\u4f48\u3002\u6700\u5f8c\u5c07 f(B)\u53ca f(A)", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u5716\u4e00\u70ba\u6240\u6709\u5b57\u7684 pcdiff \u5206\u4f48\u5716(Histogram)\u3002\u6bcf\u4e00\u500b\uf923\u8272\u9577\u689d\u5716 M i \u4ee3\u8868 pcdiff \u70ba i (i=\u2026, -0.2, -0.1, 0, 0.1, 0.2, \u2026.)\u7684\u5b57\u7684\u500b\uf969(\u9ed1\u8272\u5b57)\uff0c\u540c\u6642\u6211\u5011\u4e5f\u7d71\u8a08\u6bcf\u4e00\uf923\u8272\u9577 \u689d\u4e2d\u6709\u591a\u5c11\u500b\u5b57\u7684\u8072\u8abf\u8207\u5176\u8072\u7b26\u69cb\u4ef6\u76f8\u540c\u4f46\u8207\u975e\u8072\u7b26\u69cb\u4ef6\uf967\u540c\uff0c\u6211\u5011\u7528 N i \uf92d\u8868\u793a(\uf93d\u8272 \u5b57)\uff1b\u53e6\u5916\u6211\u5011\u4e5f\u7d71\u8a08\u6709\u591a\u5c11\u500b\u5b57\u7684\u8072\u8abf\u8207\u5176\u8072\u7b26\u69cb\u4ef6\uf967\u540c\u4f46\u8207\u975e\u8072\u7b26\u69cb\u4ef6\u76f8\u540c\uff0c\u6211\u5011 \u7528 L i \uf92d\u8868\u793a(\u7d2b\u8272\u5b57)\u3002\u9019\uf978\u90e8\u4efd\u7684\u5b57\u96c6\u5206\u5225\u4ee3\u8868\u7684\u662f\u5728\u63a1\u7528\u65b9\u7a0b\u5f0f(5)\uf92d\u8a08\u7b97\u767c\u97f3\u76f8\u4f3c \ufa01\u6642\uff0cpcdiff \u6703\u589e\u52a0\u6216\u662f\u6e1b\u5c11\u7684\u5b57\uf969\u3002\u56e0\u6b64\u5982\u679c\u6211\u5011\u63a1\u7528\u65b9\u7a0b\u5f0f(5)\uf92d\u8a08\u7b97\u767c\u97f3\u76f8\u4f3c\ufa01\uff0c \u5c07\u6703\u6709\u65b0\u589e\u52a0 N 0 +N -0.1 +\u2026 +N -\u03b4+0.1 \u53ef\u88ab\u6b63\u78ba\u9810\u6e2c\u7684\u5b57(\u6a58\u8272\u8f49\u63db)\uff0c\u4f46\u662f\u540c\u6642\u6703\u6709 L 0.1 +L 0.2 +\u2026+L \u03b4 \u7684\u5b57\u6703\u5f9e\u6b63\u78ba\u9810\u6e2c\u8f49\u70ba\u932f\u8aa4\u9810\u6e2c\u6216\u7121\u6cd5\u5224\u65b7 (\uf923\u8272\u8f49\u63db) \u3002\u56e0\u6b64\uf974 N 0 =125, L 0 =73, N -0.1 =45, N -0.2 =10, L 0.1 =25, L 0.2 =14(\u5982\u5716\u4e8c),", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": {}, | |
| "ref_entries": {} | |
| } | |
| } |