{ "paper_id": "Y10-1010", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T13:40:34.334690Z" }, "title": "Lexical Semantics-Syntactic Model for Defining and Subcategorizing Attribute Noun Class", "authors": [ { "first": "Xiaopeng", "middle": [], "last": "Bai", "suffix": "", "affiliation": {}, "email": "baixiaopeng@nus.edu.sg" }, { "first": "Hui", "middle": [], "last": "Wang", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Either word sense classification or word sense clustering can not avoid this problem: what features people can get from word meaning that can be used as the tools employed for producing word sense ontology. In this paper, we present a lexical semantic-syntactic model to define and subcategorize a noun class-attribute noun class, by acquiring syntactic behavior and semantic restrictions from corpus. First we introduce the idea of attribute noun class. Then we describe the model, which is a specific tool providing a practical method to decide whether a word should be located in a specific word sense class or not. Then we apply the model to 15 nouns to show how the model works, and create a sample taxonomy for the 15 nouns.", "pdf_parse": { "paper_id": "Y10-1010", "_pdf_hash": "", "abstract": [ { "text": "Either word sense classification or word sense clustering can not avoid this problem: what features people can get from word meaning that can be used as the tools employed for producing word sense ontology. In this paper, we present a lexical semantic-syntactic model to define and subcategorize a noun class-attribute noun class, by acquiring syntactic behavior and semantic restrictions from corpus. First we introduce the idea of attribute noun class. Then we describe the model, which is a specific tool providing a practical method to decide whether a word should be located in a specific word sense class or not. Then we apply the model to 15 nouns to show how the model works, and create a sample taxonomy for the 15 nouns.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "The meaning of a word might impact the syntactic behavior of the word. For example, attributive adjectives (also called non-predicative adjectives or as qu bie ci known in Chinese) in Chinese like \"\u96ea \u767d(snowy white)\" can not be syntactically used with adverb, so \"\u975e\u5e38(very)/adv \u96ea\u767d(snowy white)/adj\" is not a legal phrase in Chinese. Because there is a adverbial morpheme \"\u96ea(snowy)\" in word \"\u96ea\u767d(snowy white)\" which means \"snowy\", the word merges the meaning of adverb into its lexical meaning. Attribute noun can not be attributive in attributive-head relation and rarely be isolate subject in subject-predicate relation which we will show in the following section.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The semantic selectional restrictions are semantic frame in which words should appear. The semantic frame is the grammatical relation with semantic constrains. For example, there are two grammatical slots in VO relation: verbal and objective. The meaning of object must semantically match with the meaning of the verb and vice versa, in other word, either verb or object has semantic \"preferences\" to the collocate words; such \"preferences\" is \"selectional restrictions\" in lexical semantics. The selectional restrictions also exist in semantic role frame, but we do not discuss the issue in this paper.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "ANC is one of the classes making the noun ontology. There have to be some formal features to distinguish ANC with other noun classes-we have to tell how ANC different with other noun classes.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Syntactic behavior of ANC", "sec_num": "2.1" }, { "text": "The syntactic behavior, as we discussed before, is the grammatical relations in which attribute noun could appear.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Syntactic behavior of ANC", "sec_num": "2.1" }, { "text": "Generally, noun is grammatically united other words with following syntactic relations: a.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Syntactic behavior of ANC", "sec_num": "2.1" }, { "text": "Subject-predicate (SP) relation: \u4eca\u5929 \u661f\u671f\u4e00\u3002(Today is Monday.) Noun can be either subject or predicate in Chinese.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Syntactic behavior of ANC", "sec_num": "2.1" }, { "text": "Verb-object (VO) relation: \u4eca\u5929 \u4e0b\u5348 \u6253 \u7bee\u7403\u3002(Play basketball this afternoon.) Noun can be object.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "b.", "sec_num": null }, { "text": "Attributive-head (AH) relation: \u7687\u5e1d \u7684 \u65b0\u88c5\u3002(The king's new dress.) Noun can be either attributive or head.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "c.", "sec_num": null }, { "text": "Chinese is SVO language which is word-order-dependent, so we might acquire the syntactic behavior by studying the grammatical distribution of attribute noun in these grammatical relations.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "c.", "sec_num": null }, { "text": "The meaning of attribute noun presents incomplete information-it is supposed to combine with other words to make an integrated expression, in other words, the attribute noun \"looks forward to\" collocating with other words which can specify its meaning. According to this, attribute noun can not be isolate subject in SP. In sentence \"\u4eca\u5929 \u661f\u671f\u4e00(today is Monday)\", the noun \"\u4eca\u5929\" is an isolate subject in the sentence, it can be subject by itself without combining with other word. Because subject should be definite in Chinese and present integrated information, so attribute noun can not be isolate subject in a sentence. For example, a3 \u989c\u8272 \u662f \u7eff\u8272 \u7684 (color is green) the meaning of a3 presents incomplete information. It is supposed to tell the color of what thing or who so that people can understand the expression. Thus the attribute noun \"\u989c\u8272(color)\" can not be isolate subject in sentence like noun \"\u4eca\u5929(today)\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "c.", "sec_num": null }, { "text": "With the same reason, in AH relation, the attribute noun can not be the isolate attributive modifying head. Because the meaning of attribute noun needs to be specified by other words rather than specifying, thus it can be head modified by the attributive in AH. Thus, if we put a word before a3: a4 \u8863\u670d \u7684 \u989c\u8272 \u662f \u7eff\u8272 \u7684 (color of the clothes is green) the meaning of a4 is complete, we have a AH, in which attribute noun is head, to make a4 understandable.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "c.", "sec_num": null }, { "text": "Therefore, we have the syntactic behavior of ANC by studying its lexical meaning.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "c.", "sec_num": null }, { "text": ". The attribute noun can not be the isolate subject in SP.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "F1", "sec_num": null }, { "text": "F2. The attribute noun can not be the attributive in AH.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "F1", "sec_num": null }, { "text": "We will use F1 and F2 to determine whether a noun is an attribute noun or not in the section below.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "F1", "sec_num": null }, { "text": "Worth to mention that: 1) the rules of F1 and F2 is not black or white rule, the syntactic features occur with very big probability; 2) when we test whether a noun is an attribute one, the F1 and F2 should be applied in sentence level, i.e. if a noun is an isolate subject or attributive, the SP or AH must be semantically understandable as a sentence.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "F1", "sec_num": null }, { "text": "The ANC is a big word sense class and its meaning is general concept, thus we need to divide ANC into small sense classes in terms of more specific meaning. We use the semantic features to subcategorize ANC which are the semantic constrains between attribute noun and the collocate words in SP, VO and AH.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Semantic features of ANC", "sec_num": "2.2" }, { "text": "The selectional restrictions can be learned by studying the meaning of attribute noun and it's collocate words in AH, SP and VO.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Semantic features of ANC", "sec_num": "2.2" }, { "text": "Predicate tells something about the subject. In SP, predicate is the value of attribute noun. Attribute nouns with different meanings calls for predicates that with different concept. Thus, we can assign an attribute noun to more specific ANC class in terms of the meanings of predicate collocated with.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Attribute Noun Used as Subject in SP", "sec_num": "2.2.1" }, { "text": "Attribute noun is object in VO, which is one of the arguments of verb. Object semantically matches with verb as other types of arguments. For example, for the attribute noun \"\u5916\u8c8c\", it can collocate with verb with the meaning of \"watch\" like \"\u770b\" in VO (\u770b/v \u4ed6 \u7684 \u5916\u8c8c/o, watch his appearance); while for the attribute noun \"\u6027\u683c\", the verb should be with the meaning of \"mental action\" like \"\u4e86\u89e3\" (\u4e86 \u89e3/v \u4ed6 \u6027\u683c/o, know his temperament). Different attribute nouns \"tend to\" collocate with different verbs.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Attribute noun as object in VO", "sec_num": "2.2.2" }, { "text": "The function of attributive in AH is specifying the head. There are two meanings of attribute: 1) the value of attribute (\u6602\u8d35/a \u7684 \u4ef7\u683c/h, high price) which semantically resembles the predicate in 2.2.1; 2) the host which semantically possess the attribute (\u4e66/a \u7684 \u4ef7\u683c/h, price of the book). Thus the meanings of host of attribute and attribute, as well as attribute value and attribute should match each other.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Attribute noun as head in AH", "sec_num": "2.2.2" }, { "text": "The attribute nouns can be assigned to more specific ANC classes in terms of the meanings of words with which the attribute nouns collocate in SP, VO and AH. Thus the selectional restrictions in three grammatical relations can be used as subcategorization criterion for ANC.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Attribute noun as head in AH", "sec_num": "2.2.2" }, { "text": "Now we deduce a lexical semantic-syntactic model (LSS model) from above analysis for defining and subcategorizing attribute noun. We use pseudo-code of C to describe the model below: The attribute noun is presented in a structure AN, in which the syntactic behavior and selectional restrictions are stored. The function AttrDef decides whether a word is an attribute noun or not, using the syntactic features F1 and F2. Then the word is input in Subcat_AN which assigns the word to a more specific sense class. In Subcat_AN, a noun is semantically analyzed in SR function with 3 syntactic relations: AH, VO and SP. The result of syntactic and semantic analysis will be presented in structure for each input.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The lexical semantic-syntactic model for defining and subcategorizing attribute noun", "sec_num": "3." }, { "text": "Define", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The lexical semantic-syntactic model for defining and subcategorizing attribute noun", "sec_num": "3." }, { "text": "In this section, we make an experiment to evaluate the applicability of LSS model.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experiment", "sec_num": "4." }, { "text": "First we choose 15 nouns from Singapore Chinese Textbook Corpus. Then we abstract sentences from the first page result that CCL online corpus generated for each noun and acquire the data of syntactic features. The syntactic features are used to determine whether a noun is attribute noun or not.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Methods", "sec_num": "4.1" }, { "text": "Second, we choose 15 attribute nouns from the same corpus and generate sentences for each attribute noun from CCL online corpus, for abstracting semantic features. The semantic features will be put in structure AN mentioned in section 3 to show how 15 nouns differ in semantic selectional restrictions. Such semantic features help us to divide 15 attribute nouns into several subcategories.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Methods", "sec_num": "4.1" }, { "text": "The features, either of syntactic or semantic, are manually abstracted from sample sentences. We do not employ machine learning software (like Stanford Parser, SRL software) to do the job, because the purpose of this paper is to create a model for attribute noun and evaluate rather than evaluate machine learning algorithm. We want the data to verify the model. In additional, the data set is quite small (15 words).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Methods", "sec_num": "4.1" }, { "text": "The 15 nouns are: \u8001\u767e\u59d3(people), \u534e\u88d4(foreign citizen of Chinese origin), \u8336\u5ba2 (customer), \u77ff\u957f(mine manager), \u73cd\u73e0(pearl), \u7518\u6cc9(spring), \u6c14\u6d6a(airsurge), \u58f0\u97f3(sound), \u4e7e\u5764(the universe), \u524d\u81c2(forearm), \u957f\u5ea6(length), \u6307\u6807(indicator), \u5916\u8c8c(appearance), \u6027\u80fd (performance), \u76f4\u5f84(diameter). The last five nouns are already tagged as attribute noun. We use 4 indicators to show the grammatical distribution: isolate subject in SP, head in AH, Attributive in AH, object in VO. A word is called isolate subject when it is the subject of a sentence all alone, rather than in a noun phrase. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Syntactic behavior to determine whether a noun is attribute noun", "sec_num": "4.2" }, { "text": "The 15 nouns are: \u529b \u6c14 (strength), \u5929 \u5206 (talent), \u8bed \u8c03 (tone), \u751f \u547d \u529b (vitality), \u672c \u80fd (instinct), \u6b4c\u5589(voice), \u5bb6\u5883(family circumstances), \u804c\u52a1(position), \u6027\u683c(character), \u8bb0\u5fc6 \u529b(memory), \u610f\u56fe(intention), \u5634\u8138(look), \u88c5\u675f(garb), \u76f4\u5f84(diameter), \u98ce\u5ea6(grace).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The 15 attribute nouns for abstracting semantic features", "sec_num": "4.3" }, { "text": "The nouns are randomly chosen from the corpus. Table 2 shows the host word (noun) and attributes nouns (head) in AH. Table 2 Attribute nouns Host words \u529b\u6c14 (strength, physical power)", "cite_spans": [], "ref_spans": [ { "start": 47, "end": 54, "text": "Table 2", "ref_id": null }, { "start": 117, "end": 124, "text": "Table 2", "ref_id": null } ], "eq_spans": [], "section": "The 15 attribute nouns for abstracting semantic features", "sec_num": "4.3" }, { "text": "\u674e\u9035 ( (human's name), \u5973\u58eb (lady), \u653f\u6cbb\u5bb6 (politician) The hosts of attribute nouns \"\u5929\u5206(talent)\", \"\u8bed\u8c03(tone)\", \"\u6b4c\u5589(voice)\", \"\u5bb6\u5883(family circumstances)\", \"\u804c\u52a1(position)\", \"\u6027\u683c(character)\", \"\u610f\u56fe(intention)\", \"\u5634\u8138(look)\", \"\u88c5 \u675f(garb)\" and \"\u98ce\u5ea6(grace)\" are all of meaning of \"human\". These nouns can be classified as \"attribute of human\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in AH", "sec_num": "4.4" }, { "text": "The nouns \"\u529b\u6c14(strength)\", \"\u751f\u547d\u529b(vitality)\", \"\u672c\u80fd(instinct)\" have hosts of not only the meaning of \"human\" also of \"animal\". Both meanings can be presented as \"living creatures, thus the structure presentation of these nouns is: These nouns can be classified as \"attribute of living creatures\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in AH", "sec_num": "4.4" }, { "text": "\u529b\u6c14(", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in AH", "sec_num": "4.4" }, { "text": "While the nouns above can be subcategorized by the meaning of the host words, while the noun \"\u76f4\u5f84(diameter)\" can not, because there is no semantic agreement of its hosts. The hosts are of various meanings, like \"space\" ( \u94f6 \u6cb3 \u7cfb /galaxy), \"architecture\" ( \u6e29 \u5ba4 /greenhouse), and \"artificial production\" (\u624b\u69b4\u5f39/grenade).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in AH", "sec_num": "4.4" }, { "text": "The ANC can be further classified by the verb with which the attribute noun matches. The attribute nouns and verbs are showed in table 3. Table 3 Attribute The verbs governing \"\u529b\u6c14(strength)\", \"\u5929\u5206(talent)\", \"\u8bed\u8c03(tone)\", \"\u751f\u547d\u529b(vitality)\", \" \u6027 \u683c (character)\", \" \u672c \u80fd (instinct)\", \u8bb0 \u5fc6 \u529b (memory) and \" \u98ce \u5ea6 (grace)\" are not specialized, the verb governing \"\u529b\u6c14(strength)\" can also collocate with \"\u6027\u683c(character)\". Thus, we can not produce subcategories for these attribute nouns by the meaning of verbs. The nouns \"\u5634\u8138(look)\", \"\u88c5\u675f(garb)\" and have verbs with meanings that only can collocate with them: \"\u770b(look)\", \"\u7785\u7740(stare at)\". We can also find out some other verbs with same meaning for them: \"\u89c2\u5bdf(observe)\", \"\u770b\u89c1(see)\". If anything can be saw by human's eyes, watched or observed, it must has outward appearance. The structure presentation of these nouns is: Thus we create a new ANC subcategory for the two nouns: \"appearance attribute of human\".", "cite_spans": [], "ref_spans": [ { "start": 138, "end": 145, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Selectional restrictions in VO", "sec_num": "4.5" }, { "text": "\u5634\u8138(look)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in VO", "sec_num": "4.5" }, { "text": "The verbs of noun \"\u610f\u56fe(intention)\" are of meaning of \"mental processing\", so it can be presented as: Thus we generate a subcategory for it: \"mental attribute of human\". The meaning of verbs of \"\u804c\u52a1(position)\" is \"social acting\", so it can be presented as :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in VO", "sec_num": "4.5" }, { "text": "\u804c\u52a1(position)={syn_behavior[]; sr_AH[human]; sr_VO[social acting]; sr_SP[]};", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in VO", "sec_num": "4.5" }, { "text": "We create a new subcategory for it-\"social attribute of human\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in VO", "sec_num": "4.5" }, { "text": "The nouns are needed to be analyzed in SP, for the AH and VO are not work on some nouns. Table 4 below is the attribute noun and predicates. \u5f88\u9ad8 (pretty high), \u88ab\u5347\u9ad8 (be prompted) \u6027\u683c (character) \u5916\u5411 (optimistic), \u597d\u574f (good or bad) \u8bb0\u5fc6\u529b (memory) \u8870\u9000 (decline), \u5f88\u597d (very good) \u610f\u56fe (intention) \u662f\u8ba4\u771f\u7684 (is serious), \u5f88\u660e\u663e (very clear) \u5634\u8138 (look) \u66b4\u9732\u65e0\u9057 (completely reveal), \u4e4c\u9ed1 (black) \u88c5\u675f (garb) \u6df3\u6734 (simple), \u7c97\u9119 (vulgar), \u9ad8\u96c5 (elegant) \u76f4\u5f84 (diameter) 0.5-5 \u5fae\u7c73 (0.5-5 micrometers), \u5c0f\u4e8e 20 \u5343\u7c73 (less than 20 km) \u98ce\u5ea6 (grace) \u7fe9\u7fe9 (elegant), \u9ad8\u96c5 (elegant)", "cite_spans": [], "ref_spans": [ { "start": 89, "end": 96, "text": "Table 4", "ref_id": "TABREF5" } ], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "The meanings of predicates of attribute nouns \"\u529b\u6c14(strength)\", \"\u5929\u5206(talent)\", \"\u8bed\u8c03 (tone)\", \"\u751f\u547d\u529b(vitality)\", \"\u672c\u80fd(instinct)\" and \"\u8bb0\u5fc6\u529b (memory)\" are not distinguishable. The predicates like \"\u5f88\u5927 (very strong)\", \"\u5f88\u5c0f(quite weak)\", \"\u65fa\u76db(vigorous)\", \"\u5f3a (strong)\" have general meanings in semantic, which can collocate with many different attribute nouns.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "The meaning of predicates of \"\u6b4c\u5589(voice)\" are \"tone of sound\" which can not collocate with other 14 nouns, thus \"\u6b4c\u5589(voice)\" can be presented as: We get a new subcategory: \"audible attribute of human\". The meaning of predicates of \"\u5bb6\u5883(family circumstances)\" mean \"social status\" which can only collocate with the noun. \"\u5bb6\u5883(family circumstances)\": The noun can be assigned with a new subcategory: \"social attribute of human\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "\u6b4c\u5589(", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "\u5bb6\u5883", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "Meaning of predicates of \" \u98ce\u5ea6(grace)\" mean \"appearance status\" which can only collocate with the noun: The predicate of \"\u98ce\u5ea6(grace)\" and the verb of \"\u5634\u8138(look)\" and \"\u88c5\u675f(garb)\" have similar in meaning, thus \"\u98ce\u5ea6(grace)\" can be assigned in \"appearance attribute of human\". \"\u76f4\u5f84(diameter)\" takes quantifier phrase as its predicate in SP. It can be presented as: ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "\u76f4\u5f84(diameter)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Selectional restrictions in SP", "sec_num": "4.6" }, { "text": "To summary, we create a sample taxonomy for these 15 attribute nouns: ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Sample taxonomy", "sec_num": "4.7" }, { "text": "Attribute", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Sample taxonomy", "sec_num": "4.7" }, { "text": "In this paper, we present a practical method to define and subcategorize the attribute noun class, and produce sample word sense taxonomy for 15 Chinese attribute nouns. Syntactic and semantic features are employed to do the job, from which we derive a syntactic-lexical semantic model for defining and subcategorizing the word sense class. First learn the grammatical distributions of each word from corpus to determine a noun is whether attribute noun or not. Then we study the selectional restrictions between the noun and its collocate words in each grammatical relation and assign the noun to more specific sense class. Finally produce a small word sense taxonomy. We found that: 1. we get the syntactic behavior of word by studying the meaning of the word, thus sense classes (meanings with same concept) could be distinguished by syntactic behavior; 2. creating word sense class requires both syntactic and semantic features which we call \"syntagmatic features\" of word; 3. general meaning of collocate words is essential for acquiring the semantic features of word sense class, which means some coarse and solid concepts of noun, verb and adjective are needed. Such coarse and solid concepts are the precondition for studying the selectional restrictions between collocate words.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "5." } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Semantic fields and lexical structure", "authors": [ { "first": "Adrienne", "middle": [], "last": "Lehrer", "suffix": "" } ], "year": 1974, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Adrienne Lehrer, Semantic fields and lexical structure, North-Holland Press, 1974", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "English verb classes and alternations: a preliminary investigation", "authors": [ { "first": "Beth", "middle": [], "last": "Levin", "suffix": "" } ], "year": 1993, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Beth Levin, English verb classes and alternations: a preliminary investigation, University of Chicago Press, 1993", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Formal Ontology in Conceptual Analysis and Knowledge Representation", "authors": [ { "first": "Thomas", "middle": [ "R" ], "last": "Gruber", "suffix": "" } ], "year": 1993, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Thomas R. 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a man), \u7537\u4eba (man), \u84dd\u9cb8 (blue whale), \u8682\u8681 (ant), \u5c0f\u8c61 (baby elephant) \u5929\u5206 (talent) \u9f50 \u8fbe \u5185 (human's name), \u5b66 \u751f (student), \u519c \u6c11 (farmer), \u5973 \u540c \u5fd7 (female colleague), \u82ac \u5170 \u4eba (Finland people) \u8bed\u8c03 (tone) \u6731\u9555\u57fa (human's name), \u5916\u957f (minister of foreign affairs), \u603b \u7406 (prime ministry),\u4ed6 (he), \u8001 \u5e08 (teacher) \u751f\u547d\u529b (vitality) \u5fae \u751f \u7269 (microorganism), \u690d \u7269 (plant), \u867e (shrimp), \u8748\u8748 (katydid), \u7d2b\u8346\u82b1 (Chinese redbud) \u672c\u80fd (instinct) \u52a8\u7269 (animal), \u9e1f (bird), \u6bcd\u8c79 (female leopard), \u4eba (human) \u6b4c\u5589 (voice) \u4fc4 \u8033 \u752b \u65af (human's name), \u5979 (she), \u6b4c \u5531 \u5bb6(singer) \u5bb6\u5883 (family circumstances) \u62c9\u666e\u62c9\u65af (human's name), \u5b66\u751f (student), \u8d3e\u7389\u5cf0 (human's name), \u5362\u68ad (human's name) \u804c\u52a1 (work position) \u4ed6 (he), \u6587 \u6b66 \u767e \u5b98 (government officers), \u4eba \u7269 (people), \u4e2d\u6821 (lieutenant) \u6027\u683c (character) \u8001\u5e08 (teacher), \u5988\u5988 (mother), \u81ea\u5df1 (myself) \u8bb0\u5fc6\u529b (memory) \u52a8\u7269 (animal), \u4eba (human), \u60a3\u8005 (patient), \u8001\u5e74 \u4eba (old people), \u871c\u8702 (bee) \u610f\u56fe (intention) \u5bfc\u6f14 (director), \u6211 (I), \u9886\u5bfc (boss), \u6559\u7ec3 (coach) \u5634\u8138 (look) \u722a \u7259 (lackey), \u653f \u5ba2 (politician), \u7f8e \u56fd \u4eba (American), \u654c\u4eba (enemy) \u88c5\u675f (garb) \u5987\u5973 (women), \u59d1\u5a18\u4eec (ladies), \u5979 (she), \u54e8\u5175 (guard), \u5c06\u9886 (general) \u76f4\u5f84 (diameter) \u94f6 \u6cb3 \u7cfb (galaxy), \u6e29 \u5ba4 (greenhouse), \u624b \u69b4 \u5f39 (grenade), \u5751 (pit) \u98ce\u5ea6 (grace) \u7ec5 \u58eb (gentlemen), \u5b66 \u8005 (scholar), \u5d14 \u6587 \u534e", "type_str": "figure" }, "FIGREF1": { "uris": null, "num": null, "text": "noun Verb \u529b\u6c14 (strength, physical power) \u6709 (have), \u6ca1\u6709 (do not have), \u4e0b,\u8d39 (spend) \u5929\u5206 (talent) \u6709 (have), \u6ca1\u6709 (do not have), \u8bed\u8c03 (tone) \u8fd0\u7528 (handle), \u7528 (use) \u751f\u547d\u529b (vitality) \u663e\u793a (show), \u5177\u6709 (have), \u4fdd\u6301 (maintain) \u672c\u80fd (instinct) \u6709 (have), \u6210\u4e3a (become) \u6b4c\u5589 (voice) \u5c55\u793a (show), \u542c (listen to) \u5bb6\u5883 (family circumstances) NULL \u804c\u52a1 (work position) \u62c5\u4efb (occupy), \u5f00\u9664 (dismiss), \u8f9e\u53bb (retire from) \u6027\u683c (character) \u7a81\u51fa (highlight), \u8868\u73b0 (present) \u8bb0\u5fc6\u529b (memory) \u4e27\u5931 (lost), \u4fdd\u6301 (maintain) \u610f\u56fe (intention) \u9886\u4f1a (understand), \u8d2f\u5f7b (carry out), \u77e5 (know), \u63e3\u6469 (conjecture) \u5634\u8138 (look) \u63cf\u5199 (describe), \u63ed\u9732 (explosure), \u770b\u6e05 (realize), \u9732 \u51fa (reveal) \u88c5\u675f (garb) \u7a7f \u7740 (wear), \u770b (look), \u7785 \u7740 (stare at), \u770b \u6e05 (see clearly), \u76f4\u5f84 (diameter) NULL \u98ce\u5ea6 (grace) \u6ce8\u610f (pay attention to), \u8868\u73b0 (show), \u4fdd\u6301 (maintain)", "type_str": "figure" }, "FIGREF2": { "uris": null, "num": null, "text": "\u610f\u56fe(intention)={syn_behavior[]; sr_AH[human]; sr_VO[mental processing]; sr_SP[]};", "type_str": "figure" }, "FIGREF3": { "uris": null, "num": null, "text": "(family circumstances)={syn_behavior[];sr_AH[human]; sr_VO[]; sr_SP[social status]};", "type_str": "figure" }, "FIGREF4": { "uris": null, "num": null, "text": "\u98ce\u5ea6(grace)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[appearance status]};", "type_str": "figure" }, "TABREF1": { "text": "Table 1shows the grammatical distribution of the 15 nouns. Five attribute nouns show quiet distinguish grammatical distribution from other nouns: they can not be isolate subject in SP and rarely be attributive in AH. This distinguishable grammatical distribution verifies that F1 and F2 rules are valid.", "content": "
WordIsolate sub inHead in AHAttributive in AHObject in VO
SP
\u8001\u767e\u59d3/451391013
\u534e\u88d4/5000500
\u8336\u5ba2/50221846
\u77ff\u957f/50223232
\u73cd\u73e0/404141111
\u7518\u6cc9/43618217
\u6c14\u6d6a/43714
\u58f0\u97f3/49533110
\u4e7e\u5764/4291232
\u524d\u81c2/5114141310
\u957f\u5ea6/3903432
\u6307\u6807/4704502
\u5916\u8c8c/4003424
\u6027\u80fd/4204101
\u76f4\u5f84/3203200
", "html": null, "num": null, "type_str": "table" }, "TABREF2": { "text": "\u5bb6\u5883(family circumstances)= {syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]}; \u804c\u52a1(position)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]}; \u6027\u683c(character)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]}; \u610f\u56fe(intention)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]}; \u5634\u8138(look)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};", "content": "
\u88c5\u675f(garb)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};
\u98ce\u5ea6(grace)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};
The structure presentation of
these nouns is:
\u5929\u5206(talent)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};
\u8bed\u8c03(tone) ={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};
\u6b4c\u5589(voice)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[]};
", "html": null, "num": null, "type_str": "table" }, "TABREF5": { "text": "", "content": "
Attribute nounPredicate
\u529b\u6c14 (strength, physical power) \u5f88\u5927 (very strong), \u5f88\u5c0f (quite weak)
\u5929\u5206 (talent)\u9887\u9ad8 (pretty high)
\u8bed\u8c03 (tone)\u4f4e\u6c89 (low), \u5e73\u548c (gentle), \u67d4\u548c (soft), \u5e73\u7f13 (gentle)
\u751f\u547d\u529b (vitality)\u65fa\u76db (vigorous), \u5f3a (strong)
\u672c\u80fd (instinct)NULL
\u6b4c\u5589 (voice)\u5639\u4eae (loud and clear), \u6781\u5bcc\u78c1\u6027 (very charming), \u5b9b\u8f6c
(agreeable)
\u5bb6\u5883 (family circumstances)\u8d2b\u5bd2 (poor), \u5bcc\u88d5 (rich)
\u804c\u52a1 (work position)
", "html": null, "num": null, "type_str": "table" }, "TABREF6": { "text": "voice)={syn_behavior[]; sr_AH[human]; sr_VO[]; sr_SP[tone of sound]};", "content": "", "html": null, "num": null, "type_str": "table" } } } }