| { | |
| "paper_id": "Y03-1027", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T13:34:21.090963Z" | |
| }, | |
| "title": "A Large-scale Lexical Semantic Knowledge-base of Chinese", | |
| "authors": [ | |
| { | |
| "first": "Wang", | |
| "middle": [], | |
| "last": "Hui", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yu", | |
| "middle": [], | |
| "last": "Shiwen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
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| "abstract": "The Semantic Knowledge-base of Contemporary Chinese (SKCC) is a large scale Chinese semantic resource developed by the", | |
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| "paper_id": "Y03-1027", | |
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| "abstract": [ | |
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| "text": "The Semantic Knowledge-base of Contemporary Chinese (SKCC) is a large scale Chinese semantic resource developed by the", | |
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| "section": "Abstract", | |
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| "text": "All of the six sub-databases can be linked to the general database through four key fields, namely ENTRY, POS, HOMOMORPHISM and SENSE. As a result, the son knots can inherit all information from their father knots ( Figure 1 ). One of the most outstanding characteristics of SKCC is that its semantic hierarchy is based on grammatical analysis, rather than merely on general knowledge (as illustrated in Figure 2 ). This classification system represents the latest progress in Chinese semantics. It is very useful for NLP applications [21 , as well as compatible with various semantic resources, such as Wordnet [31 , Chinese concept dictionary (CCD) E41 , HowNet[51 etc. Currently, the classification of all of the 66,539 entries has already been completed. (1) Basic information of entry, such as vocabulary item, part of speech, sub-category, homograph and pronunciation; (2) Descriptions of word meaning, including sense number, definition, and semantic categories;", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 216, | |
| "end": 224, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF1" | |
| }, | |
| { | |
| "start": 404, | |
| "end": 412, | |
| "text": "Figure 2", | |
| "ref_id": null | |
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| "text": "(3) Semantic valence, thematic roles and combinatorial properties for per words; this is the most important part of SKCC and especially useful for WSD and lexical semantics research; (4) English translation and its POS tagging. If a Chinese word has two or more English counterparts, it will be regarded as different entries respectively, and the collocation information will also be given in relevant fields. This can significantly improve the quality of Chinese-English MT system. ", | |
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| "text": "In general terms, word sense disambiguation (WSD) task necessarily involves two steps: (1) the determination of all the polysemous words and homographs in the text or discourse; and (2) a means to assign each occurrence of a word to the appropriate sense.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Determination of the polysemous words and homographs", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Step (1) can be easily accomplished by reliance on SKCC. Firstly, each entry denotes one single sense of per word in SKCC. Thus, if a word has two or more senses, it will be regard as different entries, and the \"SENSE\" field will be filled with different number (as \"M\"in table 3). Table 3 Two senses of Chinese noun \"1\"", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 282, | |
| "end": 289, | |
| "text": "Table 3", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Determination of the polysemous words and homographs", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Secondly, SKCC marked all of the homographs in \"HOMOMORPHISM\" field, such as two verbs \"A-\"with different pronunciation in table 4.", | |
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| "section": "ENTRY POS SENSE", | |
| "sec_num": null | |
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| "text": "A' Kan4 A see; watch; look at see * Ka.n1 B ... look after; take care of look after Table 4 Homographs in SKCC Therefore, if either of the \"SENSE\" and \"HOMOMORPHISM\" fields is filled with value in SKCC, the entry must be a polysemous word or homograph.", | |
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| { | |
| "start": 84, | |
| "end": 91, | |
| "text": "Table 4", | |
| "ref_id": null | |
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| "eq_spans": [], | |
| "section": "ENTRY PRONUNCIATION HOMOMORPHISM DEFINITION Translation", | |
| "sec_num": null | |
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| "text": "The senses of most Chinese polysemous words and homographs belong to different semantic categories, and have different syntagmatic features in context m. SKCC gives detailed description of such information in \"AGENT\" and/or \"OBJECT\" fields as illustrated in Table 5 Polysemous adjectives in SKCC Based on the above description, the target word \"ii-VA\" in following POS-tagged text can be accurately disambiguated:", | |
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| { | |
| "start": 258, | |
| "end": 265, | |
| "text": "Table 5", | |
| "ref_id": "TABREF3" | |
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| "section": "WSD based on semantic categories", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "[1] /m 47q SIVa 1)<J/u t #*/n (A cup of light Longjing tea)", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "WSD based on semantic categories", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "[2] tit lit/t it /v 3/u A/n Thi /a , tAtin E M/d iMac,", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "WSD based on semantic categories", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "(When the season is busy, few farmers go to town and the business is rather slack)", | |
| "cite_spans": [], | |
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| "section": "WSD based on semantic categories", | |
| "sec_num": "3.2" | |
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| { | |
| "text": "In sentence [1] , the word modified by \"WA\" is the noun\" 4\" (tea) , which is a kind of \"drink\"; while the word \"MirA\" in sentence [2] is a predicate of \"business\". According to the different values in \"AGENT\" field, it is easy to judge that these two \"A\" belong to two semantic categories, viz. the former is \"light\", and the latter is \"slack\".", | |
| "cite_spans": [ | |
| { | |
| "start": 12, | |
| "end": 15, | |
| "text": "[1]", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 130, | |
| "end": 133, | |
| "text": "[2]", | |
| "ref_id": "BIBREF1" | |
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| "section": "WSD based on semantic categories", | |
| "sec_num": "3.2" | |
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| "text": "As for the polysemous words or homographs belonging to the same semantic category, the difference between them usually manifests at the collocation level. According to a study in cognitive science, people often disambiguate word sense using only a few other words in a given context (frequently only one additional word) [8] . Thus, the relationships between one word and others can be effectively used to resolve ambiguity. For example, Chinese verb \"lir has two senses: one is \"4-1r (look for) and the other is \"WI\" (give change). Only when the verb co-occurs with the noun \"a\" (money), it can be interpreted as \"give change\"; Otherwise, it means \"look for\" (see table 6 ). Table 6 Different senses of verb \"Ir According to table 6, the verb \"1r in sentence [1] below must be \"look for\", because its object is \"A\" (person), a kind of \"entity\"; while \"Irin sentence [2] has two objects, namely, indirect object \"a\" (me) and direct object \"a\"(money). Thus, its meaning is \"give change\".", | |
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| "start": 321, | |
| "end": 324, | |
| "text": "[8]", | |
| "ref_id": "BIBREF7" | |
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| "start": 760, | |
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| "text": "[1]", | |
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| "start": 867, | |
| "end": 870, | |
| "text": "[2]", | |
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| "ref_spans": [ | |
| { | |
| "start": 665, | |
| "end": 672, | |
| "text": "table 6", | |
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| }, | |
| { | |
| "start": 676, | |
| "end": 683, | |
| "text": "Table 6", | |
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| } | |
| ], | |
| "eq_spans": [], | |
| "section": "WSD based on collocation information", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "[1]/Itinr 44/c1 ffi t/v /v Lin. (They will go out to look for sb.) [2] 'WR5-34/n i/d tt4/d /v 1/r gn 19E. (The seller has not given change to me)", | |
| "cite_spans": [ | |
| { | |
| "start": 67, | |
| "end": 70, | |
| "text": "[2]", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ENTRY HOMOMORPHISM DEFINITION AGENT OBJECT DATIVE Translation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "By making full use of SKCC and a large scale POS-tagged corpus of Chinese, a multi-levels WSD model is developed and has already been used in a Chinese-English MT application.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ENTRY HOMOMORPHISM DEFINITION AGENT OBJECT DATIVE Translation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "SKCC is a well-structured Chinese-English bilingual semantic resource, as described in the paper, it has more than 66,000 Chinese words and their English counterparts classified, and the accurate description of about 1.5 million attributes further enriched the abundance of lexical semantic knowledge. It not only provides a deductive system of word meaning and valuable semantic knowledge for Chinese language processing, but also has great theoretical significance in lexical semantics and computational lexicography research.", | |
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| "section": "Conclusion", | |
| "sec_num": "4" | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "We appreciate all the members participated in SKCC project, especially Prof. Lu Jianming, Mr. Li Kangnian and Dr. Chang Baobao. The blithesome collaboration with Dr. Ying Chenjin and Mr. Guo qingjian from Chinese Department is memorable for all of us. Lastly, thanks our colleagues and friends for their kindly discussion with the authors.", | |
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| "section": "Acknowledgement", | |
| "sec_num": null | |
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| "text": "an important role in many areas of Natural Language Processing (NLP). The Institute of Computational Linguistics (ICL) of Peking University has been engaged in research and development of the Semantic Knowledge-base of Contemporary Chinese (SKCC) in the last eight years. This lexicon-building project was collaboration with the Institute of Computing Technology, Chinese Academy of Sciences during 1994-1998, and resulted in a machine-readable bilingual lexicon suitable for use with Machine Translation applications, which contained a fairly complete characterization of the semantic classification, valence specifications and collocation properties for 49 thousands Chinese words and their English counterparts [13. Since 2001, the further development of SKCC has been co-conducted by ICL and Chinese Department of Peking University. At present, SKCC has made great progress. Not only is the scale extended to 66,539 entries, but also the quality has been immensely improved. The semantic classification in the updated edition of SKCC is the embodiment of the very latest progress in Chinese linguistics and language engineering, while the semantic descriptions are comprehensive and thorough. It can provide rich lexical semantic information for various NLP applications. * Supported by China National Fundamental Research Program (973) (PN: G1998030507-4 & G1998030507-1).", | |
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| "FIGREF1": { | |
| "type_str": "figure", | |
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| "text": "Main structure of SKCC 2.2 Semantic Hierarchy", | |
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| "FIGREF3": { | |
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| "text": "Corpus-derived authentic examples of a word in context, showing how it is used, how phrases are formed around it, and so on.3 Application in WSDAs a large-scale lexical knowledge base, SKCC combines the features of many of the other resources commonly exploited in NLP work: it includes definitions and English translations for individual senses of words within it, as in a bilingual dictionary; it organizes lexical concepts into a conceptual hierarchy, like a thesaurus; and it includes other links among words according to several semantic relations, including semantic role, collocation information etc. As such it currently provides the broadest set of lexical information in a single resource. The kind of information recorded and made available through SKCC is of a type usable for various NLP applications, including machine translation, automatic abstraction, information retrieval, hypertext navigation, thematic analysis, and text processing.In this section, we shall focus on the automatic disambiguation of Chinese word senses involving SKCC since it is most troublesome, and essential for all the above NLP applications [61.", | |
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| "text": ", meat...etc. dish V -(meat or fish) I IN -------(four dishes and a bowl of soup)", | |
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| "content": "<table><tr><td>Database Name</td><td>Entries</td><td>Attribute fields</td><td/><td>Attribute value</td></tr><tr><td>nouns</td><td>38,478</td><td>15</td><td/><td>576,555</td></tr><tr><td>verbs</td><td>21,142</td><td>16</td><td/><td>338,272</td></tr><tr><td>adjective</td><td>5,577</td><td>15</td><td>,</td><td>83,655</td></tr><tr><td>pronouns</td><td>236</td><td>15</td><td/><td>3,540</td></tr><tr><td>adverbs</td><td>997</td><td>11</td><td/><td>10,967</td></tr><tr><td>numerals</td><td>109</td><td>11</td><td/><td>1,199</td></tr><tr><td>General</td><td>66,539</td><td>8</td><td/><td>532,312</td></tr><tr><td>Total</td><td>133,078</td><td>91</td><td/><td>1,546,500</td></tr><tr><td/><td colspan=\"2\">Table 1 Scale of SKCC</td><td/><td/></tr></table>", | |
| "text": "Outline of SKCC 2.1 Scale and Structure SKCC consists of one general database and six sub-databases. The general database contains shared attributes of all the 66,539 entries, while the sub-databases provide detailed descriptions of the distinctive semantic attributes associated with the parts of speech (POS). For example, the verb database has 16 attribute fields, noun database and adjective database has 15 attribute fields respectively (seetable 1)." | |
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| "content": "<table><tr><td>event -change weather -body functions perception consumption -motion creation contact possession communication competition social behavior other event (4) adverbs degree range time location frequency manner negation modality (5 ) numerals cardinal number ordinal number amount auxiliary Figure 2 Semantic hierarchies in SKCC -measurable valu immeasurable value-vision concentration -speed -temperature -speed -length -height -width -depth rigidity -humidity -thickness -tightness -size value -tactility -tone -taste -shape -quality -content -color property of human age -character -relation -condition property of space one dimension -two dimension -three dimension property of time VALUE ENTRY Commonly used Chinese word or idiom phrase PRONUNCIATION Chinese Pinyin with tones such as \"chi3zi5\" for \"R-T.\"(ruler) PART OF SPEECH POS tagging of per word or idiom SUB-CATEGORY Sub-category tagging of per word or idiom POSs All POS tagging of per word HOMOMORPHISM Homograph number SENSE Sense number of per polysemous word DEFINITION Sense definition SEMANTIC CATEGOIC Semantic categories of per word or idiom. A word can be tagged AGENT Actor of action or motion. OBJECT Object of action. DATIVE Beneficiary or suffer of action. TRANSLATION English counterpart of per word or idiom. ECAT POS tagging of per English word or phrase. ILLUSTRATIONS Corpus-derived example sentences showing authentic 2.3 FIELD contexts of a word or idiom.</td></tr></table>", | |
| "text": "Comprehensive Semantic DescriptionsThere is close correlation between lexical meaning and its distribution. Oriented to MT and Natural Language Understanding applications, SKCC can provide detailed semantic description and collocation behavior that in many cases is likely to be uniquely associated with a single sense. For.11.11.111.1 example, following attribute fields have already been filled with values in the verb database(see table 2)." | |
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| "text": "Semantic attributes in the verb database of SKCCTo sum up, the above attributes fall into five kinds of information below:" | |
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| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>below.</td></tr></table>", | |
| "text": "" | |
| } | |
| } | |
| } | |
| } |