| { |
| "paper_id": "W06-0105", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T04:05:58.704377Z" |
| }, |
| "title": "Semantic Analysis of Chinese Garden-Path Sentences", |
| "authors": [ |
| { |
| "first": "Yaohong", |
| "middle": [], |
| "last": "Jin", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Chinese Academy of Sciences", |
| "location": { |
| "postCode": "100080", |
| "settlement": "Beijing", |
| "country": "China" |
| } |
| }, |
| "email": "jinyaohong@hotmail.com" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This paper presents a semantic model for Chinese garden-path sentences. Based on the Sentence Degeneration model of HNC theory, a garden-path can arise from two types of ambiguities: SD type ambiguity and NP allocated ambiguity. This paper provides an approach to process garden-paths, in which ambiguity detection and analysis take the place of revision. The performance of the approach is evaluated on a small manually annotated test set. The results show that our algorithm can analyze Chinese garden-path sentences effectively.", |
| "pdf_parse": { |
| "paper_id": "W06-0105", |
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| "abstract": [ |
| { |
| "text": "This paper presents a semantic model for Chinese garden-path sentences. Based on the Sentence Degeneration model of HNC theory, a garden-path can arise from two types of ambiguities: SD type ambiguity and NP allocated ambiguity. This paper provides an approach to process garden-paths, in which ambiguity detection and analysis take the place of revision. The performance of the approach is evaluated on a small manually annotated test set. The results show that our algorithm can analyze Chinese garden-path sentences effectively.", |
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| "section": "Abstract", |
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| "text": "A characteristic of garden-path sentences is that they contain a temporarily ambiguous verb structure, in which a participle is confused with the main verb of the sentence. For example, consider the sentence While Anna dressed the baby spit up on the bed. Initially the baby is assumed to be the object of dressed, but when spit up is encountered, some sort of error arises. This initial process, then, must be somehow revised. This paper models the phenomenon of garden-path sentences in Chinese and addresses the mechanisms of semantic analysis. Let v1 be the first verb in the sentence and v2 the second verb. Modeling the garden-path that arising from two verbs like v1 and v2 will the focus of this paper.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "Models of reanalysis, which concern the syntactic relationships between the error signal and the head of the phrase that has been misanalyzed (Frazier, 1998) , attempt to explain how the revisions take place. However, for Chinese gar-den-path sentence analysis, the syntactic relationship is not enough because the same syntactic relationship can have different semantic interpretations. For example, \u54ac \u6b7b \u730e \u4eba \u7684 \u72d7 which is temporarily ambiguous in Chinese has different interpretations in the following two sentences. In the first sentence \u72d7(dog) is the subject of \u54ac\u6b7b(killed), and in the second it is the object of \u54ac\u6b7b(killed).", |
| "cite_spans": [ |
| { |
| "start": 142, |
| "end": 157, |
| "text": "(Frazier, 1998)", |
| "ref_id": "BIBREF0" |
| } |
| ], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "(1) \u54ac\u6b7b\u730e\u4eba\u7684\u72d7\u9003\u8dd1\u4e86 (The dog, which killed the hunter, had run away).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "(2) \u54ac\u6b7b\u730e\u4eba\u7684\u72d7\u662f\u718a\u9003\u8dd1\u7684\u552f\u4e00\u51fa\u8def (It is the only way for the bear to run away that killed the hunter' s dog).", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "So, semantic analysis is important for Chinese garden-path sentences. In this paper, garden-path sentences will be modeled using the Sentence Degeneration model (SD) of the Hierarchical Network of Concepts theory (HNC) (Huang, 1998; 2004) . Furthermore, our analysis algorithm, in which ambiguity analysis takes the place of a revision process, is introduced. We evaluated the model and the algorithm using a small sentence set with grammatical structures like NP1+V1+NP2+v2+NP3. The experiment results show that our algorithm can efficiently process Chinese garden-path sentences.", |
| "cite_spans": [ |
| { |
| "start": 219, |
| "end": 232, |
| "text": "(Huang, 1998;", |
| "ref_id": "BIBREF4" |
| }, |
| { |
| "start": 233, |
| "end": 238, |
| "text": "2004)", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "In the rest of this paper: Section 2 discusses previous work. Section 3 gives a detailed definition of the Sentence Degeneration model. Section 4 describes in detail the Semantic Model of Chinese garden-path sentences. Section 5 describes the algorithm and section 6 gives evaluation results. Section 7 presents our conclusions and future work.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "The phenomenon of garden-path sentences has attracted a lot of attention in the research com-munities of psycholinguistics and computational linguistics. The goal of this research is to discover how people understand garden-path sentences and how to analyze them automatically.", |
| "cite_spans": [], |
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| "section": "Previous Work", |
| "sec_num": "2" |
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| "text": "In English, garden-path sentences always involve a subordinate clause and a main clause together with an NP that attaches initially to the former but ultimately to the latter (Karl and Fernanda, 2003) . This NP is the point of misunderstanding and the verb after the NP is always the error signal. Models of reanalysis are aimed at describing and motivating the mechanisms used by the sentence comprehension system to detect errors, deduce useful information about the nature of the necessary repair of those errors, and ultimately create a successful analysis (Ferreira and Christianson, 2001) . Fodor and Inoue(1998) proposed the principles of Attach Anyway and Adjust to explain how reanalysis processes operate. Ferreira and Christianson(2001) stated that Reflexive Absolute Transitive (RAT) verbs, such as wash, bathe, shave, scratch, groom, and so on, are likely to give rise to garden-paths. Michael J. Pazzani(1984) demonstrated how to reanalyze one type of garden-path sentence that arises from a passive participle and a main verb conflicting. However Ferreira and Henderson(2001) demonstrated that reanalysis is more difficult when the head of the misanalyzed phrase (baby in the baby that was small and cute) is distant from the error signal.", |
| "cite_spans": [ |
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| "start": 175, |
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| "text": "(Karl and Fernanda, 2003)", |
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| "text": "(Ferreira and Christianson, 2001)", |
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| "text": "Fodor and Inoue(1998)", |
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| "text": "Ferreira and Christianson(2001)", |
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| "text": "Michael J. Pazzani(1984)", |
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| "end": 1090, |
| "text": "Ferreira and Henderson(2001)", |
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| "section": "Previous Work", |
| "sec_num": "2" |
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| { |
| "text": "In Chinese, there has been little research that directly addresses the problem of garden-paths. Zhiwei Feng(2003) interpreted the temporarily ambiguous verb structure in a garden-path in two ways; one is as a subordinate clause (MV), the other is a Reduced Relative (RR). He defined Garden Path Degree (GPD) as MV/RR. He studied some types of temporarily ambiguous verb structures such as NP1+VP+NP2+de+NP3, VP+NP1+de+NP2, V+Adj+de+N and V+V+de+N, and stated that when GPD is larger than 3, the temporarily ambiguous verb structure may give rise to a garden-path. Moreover he used the Earley algorithm to process garden-path sentences.", |
| "cite_spans": [ |
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| "start": 103, |
| "end": 113, |
| "text": "Feng(2003)", |
| "ref_id": "BIBREF6" |
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| "section": "Previous Work", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The Sentence Degeneration model, which is one model of the Hierarchical Network of Concepts theory (HNC), focuses on representing the subordinate clause in a sentence. The theory of the Hierarchical Network of Concepts (HNC theory), founded by Prof. Zengyang Huang of the Insti-tute of Acoustics, Chinese Academy of Sciences, is a theoretical approach to Natural Language Processing (NLP). The objective of HNC is to establish natural language representation patterns based on the association veins of concepts, which can simulate the language perception process of the human brain and can be applied to computational Natural Language Understanding.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Sentence Degeneration (SD) represents the semantic patterns of the subordinate clause in a sentence. There are three types of SD: prototype SD, key-element SD, and packed SD.", |
| "cite_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "In Prototype SD a subordinate clause wholly acts as one role of the other sentence without any alteration. For example, \u4e2d\u56fd\u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4 \u7ec7(China joined the WTO) is a complete sentence. However in sentence (3) this sentence acts as the subject of \u4fc3\u8fdb(accelerate). Unlike English, in Chinese there is no relative pronoun, such as that or which, to indicate that this is a subordinate clause. This phenomenon is named Prototype SD.", |
| "cite_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "(3) \u4e2d\u56fd\u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7\u4f1a\u4fc3\u8fdb\u5168\u7403\u7ecf\u6d4e \u7684\u53d1\u5c55 (That China joined the WTO will accelerate the development of global economics.).", |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Key-element SD involves an NP which semantically is an attributive clause. For example, although in sentence (4) \u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7\u7684 \u4e2d\u56fd is an NP, it can be transformed from the sentence \u4e2d\u56fd\u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7 by moving the subject \u4e2d\u56fd to the tail and adding the Chinese word \u7684(of or ' s) in front of it. We look at this NP as a specific attributive clause 1 in Chinese, and look at \u4e2d\u56fd as the core concept of this clause. Because the core concept of this clause is the subject, which is the key element, this phenomenon is called key-element SD. Besides the subject, the object and the verb of the sentence can be the core of key-element SD. For example, in sentence (5) \u4e2d\u56fd\u5bf9\u4e16\u754c\u7ecf\u6d4e\u7684\u5f71 \u54cd is one key-element SD transformed from the sentence \u4e2d\u56fd\u5f71\u54cd\u4e16\u754c\u7ecf\u6d4e, and the verb \u5f71 \u54cd is its core.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "(4) \u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7\u7684\u4e2d\u56fd\u5c06\u4e25\u683c\u9075\u5b88\u8d38 \u6613\u89c4\u5219 (China, which joined WTO, will strictly confirm the world trade rule.) (5) \u8fd9\u4e00\u5207\u4f53\u73b0\u4e86\u4e2d\u56fd\u5bf9\u4e16\u754c\u7ecf\u6d4e\u7684\u5f71\u54cd (Everything of all reflected the influence that China economics impacts on the world).", |
| "cite_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Packed SD is also an NP in which the attrib-uter is a prototype SD or key-element SD. For example, in sentence (6) and (7) both noun phrases \u4e2d\u56fd\u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7\u7684\u6d88\u606f and \u4e2d\u56fd\u5bf9\u4e16\u754c\u7ecf\u6d4e\u7684\u5f71\u54cd\u7a0b\u5ea6 are Packed SD' s. Moreover, the attributer of \u6d88\u606f is \u4e2d\u56fd\u52a0\u5165\u4e16 \u754c\u8d38\u6613\u7ec4\u7ec7 which is a prototype SD, and the attributer of \u7a0b\u5ea6 is \u4e2d\u56fd\u5bf9\u4e16\u754c\u7ecf\u6d4e\u7684\u5f71\u54cd which is a key-element SD. The words \u6d88\u606f and \u7a0b\u5ea6 are called packed words. (6) \u4e2d\u56fd\u52a0\u5165\u4e16\u754c\u8d38\u6613\u7ec4\u7ec7\u7684\u6d88\u606f\u4ee4\u4eba\u6fc0\u52a8 (The news that China joined WTO is exciting.) (7) \u4e2d\u56fd\u5bf9\u4e16\u754c\u7ecf\u6d4e\u7684\u5f71\u54cd\u7a0b\u5ea6\u5c06\u8d8a\u6765\u8d8a\u5927 (The degree of influence that Chinese economics impacts on the world is deeper and deeper.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Let ElJ be the semantic structure of the subordinate clause, GBKi be the subject/object, and El be the verb of the clause. The semantic pattern of the clause can be given as ElJ=GBK1+El+GBK2+GBK3, where GBK2 and GBK3 can be absent and the position of GBKi can be changed. Suppose ElJ-GBKi stands for the action of subtracting the GBKi from ElJ, ElJ-El stands for subtracting the El. The semantic patterns of SD can be given as follows: 1. ElJ. It means that ElJ is a prototype SD. 2. (ElJ-GBKi)+\u7684+GBKi. It means that this key-element SD can be transformed from the clause ElJ by moving GBKi to the tail and adding the Chinese word \u7684 in front of GBKi. 3. (ElJ-El)+ \u7684 +El. It means that this key-element SD can be transformed from the clause ElJ by moving El to the tail and adding the Chinese word \u7684 in front of El. Although this El looks like a noun because there is Chinese word \u7684 in front of it, it is regarded as a verb when restored back to the ElJ. 4. a prototype SD or key-element SD+{ \u7684 }+noun. It means the three patterns above can serve as the attributer of the packed SD.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Although the key-element SD and packed SD look like NP' s in Chinese, they need to be transformed back into clauses during semantic analysis. It means that in patterns 2 and 3 the GBKi and El have to be restored into ElJ. This is why we named these phenomena Sentence Degeneration. Moreover, in patterns 2 and 3, the Chinese word \u7684 is necessary to indicate the transformation, and we call it a sign of SD.", |
| "cite_spans": [], |
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| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Therefore, if an NP or other structure includes a verb and the Chinese word \u7684, it has to be analyzed as one type of SD. These semantic patterns of SD are useful for describing the interpretation of temporarily ambiguous verb structures, such as those in garden-path sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Sentence Degeneration model (SD)", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Based on the Sentence Degeneration model, there are two types of Chinese Garden-Path Sentences: SD type ambiguity garden-paths and NP allocated ambiguity garden-paths.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "A temporarily ambiguous verb structure in a sentence always has more than one semantic interpretation that can be represented as a type of SD. This phenomenon we call SD type ambiguity. If an SD type ambiguity includes a prototype SD, a garden-path arises. For example, an ambiguous structure like \u54ac\u6b7b\u730e\u4eba\u7684\u72d7 has two different interpretations as A and B in sentence (1) and(2):", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "A. It is a key-element SD in sentence (1), where \u72d7(dog) is the subject of \u54ac\u6b7b(kill), and \u730e\u4eba(hunter) is the object of \u54ac\u6b7b(kill).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "B. It is a prototype SD in sentence 2, where \u72d7(dog) is the object of \u54ac\u6b7b(kill), and \u730e\u4eba (hunter) is the attributer of \u72d7(dog).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Obviously, \u54ac\u6b7b\u730e\u4eba\u7684\u72d7 has SD type ambiguity, and one type of SD is prototype SD. Therefore, sentence (1) and sentence (2) are garden-path sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "An NP allocated ambiguity garden-path is a sentence in which one NP can be both the object of v1 and the subject of v2. Given the structure NP1+v1+NP2+v2+NP3, if NP1+v1+NP2 is a clause, NP2+v2+NP3 is a clause, too; there is an ambiguity about whether NP2 serves as either the object of the first clause or the subject of the second clause. Unlike the garden-path that arises from an SD type ambiguity, NP allocated ambiguity garden-paths confuse the main verb of the sentence. For example, Sentence (8) has two different interpretations as A and B. The difference in the two interpretations is the role of the solution. So, sentence (8) is a garden-path sentence with an NP allocated ambiguity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "(8)\u8fd9\u4e2a\u5b66\u751f\u5fd8\u8bb0\u7b54\u6848\u5728\u4e66\u7684\u80cc\u540e (The student forgot the solution was in the back of the book.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "A. the solution is the subject of was, the main verb is forgot; the solution was in the back of the book, which is a prototype SD, is the object of forgot.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "B. the solution is the object of forgot, the main verb is was, the student forgot the solution, which is a prototype SD, is the subject of was.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "We can see that it is necessary for both types of garden-path that NP1+v1+NP2 be a clause. If there is an NP allocated ambiguity garden-path, NP1+v1+NP2 is a clause together with NP2+v2+NP3 as a clause. If there is an SD type ambiguity garden-path, NP1+v1+NP2 has to be a prototype SD together with one of other two types of SD (Key-element SD or packed SD). Thus, this clause, NP1+v1+NP2, is called a garden-path detecting signal.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Therefore, in our model the garden-path is represented as one of two types of ambiguity: the SD type ambiguity and NP allocated ambiguity. Garden-path processing involves detecting and analyzing these two types of ambiguities.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Model of Chinese Garden-Path Sentence", |
| "sec_num": "4" |
| }, |
| { |
| "text": "A Chinese Garden-Path Sentence is processed in four steps:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithm for processing Chinese Garden-Path Sentences", |
| "sec_num": "5" |
| }, |
| { |
| "text": "(1) Initially, v1 is analyzed as the main verb.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithm for processing Chinese Garden-Path Sentences", |
| "sec_num": "5" |
| }, |
| { |
| "text": "(2) When v2 is encountered, if there is a clause before v2, this is a garden-path detecting signal. It is necessary to detect and analyze the garden-path in this sentence. (3) Detect if v1 and v2 can give rise to a garden-path (see section 5.1). (4) Determine the main verb of the sentence and the semantic interpretation of the garden-path sentence (see section 5.2).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Algorithm for processing Chinese Garden-Path Sentences", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Given an input string S, suppose its grammatical structure is NP1+v1+NP2+v2+NP3, where NP1 and NP3 can be absent. Therefore, a garden-path detecting signal means that NP1+v1+NP2 is a clause.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "The garden-path can be detected in two steps as follows:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Step 1: test if there is SD type ambiguity in NP1+v1+NP2.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "We can look at the clause NP1+v1+NP2 as a prototype SD without any change. If this prototype SD can be analyzed as another type of SD, such as key-element or packed SD, an SD type ambiguity is found, and the input S is a garden-path sentence. Otherwise, if there is no SD type ambiguity, the input S is a non garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "As mentioned above, sentence (1) has an ambiguity between a prototype and a key-element SD, and it is a garden-path sentence. Consider another sentence (9), with grammatical structure NP1+v1+NP2+v2. Because the Chinese word \u7684(of) in NP2 is a sign of SD, the structure can be rewritten as NP1+v1+NP21+\u7684+NP22+v2.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "(9) \u5c0f\u738b\u7814\u7a76\u9c81\u8fc5\u7684\u6587\u7ae0\u53d1\u8868\u4e86 (The paper which Mr. wang research on Luxun is published.) The structure NP1+v1+NP21+\u7684+NP22 can be analyzed in two different ways as follows. Obviously there is an ambiguity between prototype SD and packed SD, and sentence (9) is a garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "A. It is a prototype SD, where \u6587\u7ae0(paper) is the object of \u7814\u7a76(research), and \u6587\u7ae0(paper) was written by \u9c81\u8fc5(Luxun).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "B. It is a packed SD, where \u9c81\u8fc5(Luxun) is the object of \u7814\u7a76(research), and \u6587\u7ae0(paper) was written by \u5c0f\u738b(Mr. wang).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Although the structure v1+NP2+v2 in sentence (1) and the structure NP1+v1+NP2+v2 in sentence (9) can give rise to garden-paths, not all the instances of these two structures are like this. For example, in sentence group (10) \u5e74\u8f7b\u4eba (younger) and \u5200(knife) disfavor being objects of \u70ed\u7231(love) and \u524a(peel), so \u70ed\u7231\u7956\u56fd\u7684\u5e74 \u8f7b\u4eba is only analyzed as a key-element SD, and \u5c0f\u738b\u524a\u82f9\u679c\u7684\u5200 is only analyzed as a packed SD. There is no garden-path detecting signal, so these sentences are non garden-path sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "(10) \u70ed \u7231 \u7956 \u56fd \u7684 \u5e74 \u8f7b \u4eba \u56de \u56fd \u4e86 (The younger who love his country go back.) \u5c0f \u738b \u524a \u82f9 \u679c \u7684 \u5200 \u4e0d \u89c1 \u4e86 (The knife with which Mr. wang peeled the apple is lost.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Furthermore, in sentence group (11), v1+NP2 is a clause, so there is a garden-path detecting signal. However, \u76ae(fruit skin) disfavors being the subject of \u524a(peel), and \u95e8(door) is not a packed word, so \u524a\u82f9\u679c\u7684\u76ae and \u5c0f\u738b\u63a8\u5f00\u623f \u95f4 \u7684 \u95e8 are only analyzed as prototype SD. There is no SD type ambiguity, so these sentences are non garden-path sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "(11) \u524a\u82f9\u679c\u7684\u76ae\u8981\u5c0f\u5fc3 (Peeling the apple need to be careful.) \u5c0f\u738b\u63a8\u5f00\u623f\u95f4\u7684\u95e8\u8d70\u4e86 (Mr. wang opened the door and went away.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Step 2: test if NP2+v2+NP3 is a clause. If NP2+v2+NP3 is not a clause, definitely there is no NP allocated ambiguity, and the sentence is not a garden-path sentence. For example, in sentence (12) \u4f0a\u62c9\u514b\u8d77\u56e0\u4e8e\u80fd\u6e90\u5371\u673a(Iraq is due to the crisis of energy) is not a clause, so sentence (12) is a non garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "(12) \u7f8e\u56fd\u6253\u51fb\u4f0a\u62c9\u514b\u8d77\u56e0\u4e8e\u80fd\u6e90\u5371\u673a (That USA attacked Iraq is due to the crisis of energy)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "If NP2+v2+NP3 is a clause, there are two interpretations for v1 and v2.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "First, v1 and v2 are serial verbs, and the sentence can be divided into two separate sentences; one is NP1+v1+NP2, the other is NP2+v2+NP3 and the subject of v2 is NP2. For example, sentence (13) can be divided into sentences 14and (15). This phenomenon can be interpreted as sentence (15) sharing \u5927 \u4f1a (conference) with sentence 14, which is not NP allocated ambiguity, so the sentence (13) is a non garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "(13) \u6587\u4ef6\u5c06\u63d0\u4ea4\u7ed9\u5927\u4f1a\u8ba8\u8bba (The file will be given to the conference to discuss.) (14) \u6587\u4ef6\u5c06\u63d0\u4ea4\u7ed9\u5927\u4f1a (The file will be given to the conference.) (15) \u5927 \u4f1a \u8ba8 \u8bba \u8fd9 \u4e2a \u6587 \u4ef6 (The conference will discuss the file.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Second, one of v1 and v2 is the main verb of the sentence, and NP2 has to be in NP1+v1+NP2 or NP2+v2+NP3, and cannot be shared. For example, in sentence (8), the solution cannot be shared by forgot and was. Absolutely this is an NP allocated ambiguity, and the sentence is a garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "The difference between a serial verb interpretation and an NP allocated ambiguity interpretation is the semantic information of the two verbs. Suppose VS(pro) is the set of all verbs whose subject can be a prototype SD, VO(pro) is the set of all verbs whose object can be a prototype SD. Verbs about mental acts, emotions or other human feelings, such as forget, worry, cry, belong to the VS(pro). Verbs about propositions, causes and results, such as be, result in, be due to, belong to both VO(pro) and VS(pro). If NP2+v2+NP3 is a clause, and if v1 is not one of VO(pro) and v2 is not one of VS(pro), the sentence is a non garden-path sentence and these two verbs are serial verbs. Otherwise, the sen-tence is a garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path detection", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "A garden-path is always affected by the selection of the main verb of a sentence. In the garden-path caused by SD ambiguity, v1 is regarded as the main verb initially, however, in the end, v2 is the real main verb. In the garden-path caused by NP allocated ambiguity, both v1 and v2 can be the main verb. So, the garden-path analysis includes two steps: the first step is determining the main verb of the sentence; the second step is disambiguating the SD type or the NP allocated ambiguity, and determining the semantic structure of the sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "Given a garden-path sentence with grammatical structure NP1+v1+NP2+v2+NP3, the analysis process is as follows:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "First, if an SD type ambiguity is detected, it means NP1+v1+NP2 can be a prototype SD and key-element or packed SD, and v2 always is the main verb of the sentence. The ambiguity can be processed as in Figure 1 . For example, In sentences (1) and 2, \u662f(is) is one of VS(pro) and \u9003\u8dd1(run away) is not, so \u54ac\u6b7b\u730e\u4eba\u7684\u72d7 is processed as a key-element SD in sentence (1) and a prototype SD in sentence (2).", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 201, |
| "end": 209, |
| "text": "Figure 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "Second, if an NP allocated ambiguity is detected, it means that both NP1+v1+NP2 and NP2+v2+NP3 can be clauses. The main verb can be determined in Figure 2 . The NP allocated ambiguity can be processed as in Figure 3 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 146, |
| "end": 154, |
| "text": "Figure 2", |
| "ref_id": null |
| }, |
| { |
| "start": 207, |
| "end": 215, |
| "text": "Figure 3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "(16) \u5f20 \u5148 \u751f \u770b \u89c1 \u674e \u5c0f \u59d0 \u6b63 \u5728 \u8df3 \u821e (Mr. Zhang saw Miss. Li dancing.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "The result of garden-path analysis is a semantic structure for the sentence. In Figures 1 and 3 , a flag of prototype SD, key-element SD and packed SD, which indicates the semantic interpretation, is added to the grammatical structure of the sentence. Therefore, the main verb, which is always outside the flag, and the semantic structure of the sentence are both represented.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 80, |
| "end": 95, |
| "text": "Figures 1 and 3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Garden-path analysis", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "NP1+v1+NP2 is Sentence semantic structure Example one of VS(pro) not one of VS(pro) not one of VS(pro) a prototype SD a key-element SD a packed SD (NP1+v1+NP2)+v2+NP3 <NP1+v1+NP2>+v2+NP3 {NP1+v1+NP2}+v2+NP3 Sentence (2) Sentence (1) Sentence (9) Figure 1 : The process of SD type ambiguity in garden-path sentences. Where ( ) is the flag indicating that the content in it is prototype SD, < > is the flag of key-element SD, and { } is the flag of packed SD.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 246, |
| "end": 254, |
| "text": "Figure 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "v2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "v2 is the main verb is Example one of VO(pro) one of VO(pro) not one of VO(pro) not one of VS(pro) one of VS(pro) one of VS(pro) v1 prior to be v1 v2 Sentence (16) Sentence (8) Sentence (12) Figure 2 : The main verb determining in garden-path sentences. Here prior to means that if v1 is one of VO(pro) and v2 is one of VS(pro), the main verb is v1 in most cases. In some cases it depends on the meaning of v1 and v2, whether v1 is the main verb. These cases are out of the scope of consideration of this paper.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 191, |
| "end": 199, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "V1 is", |
| "sec_num": null |
| }, |
| { |
| "text": "Sentence semantic structure", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "Example Comment v1", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "The subject of v2", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "NP1+v1+(NP2+v2 +NP3) Sentence (8),", |
| "eq_num": "(16)" |
| } |
| ], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "NP2+v2+NP3 is a prototype SD, this SD is the object of v1. v2", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "The object of v1 (NP1+v1+NP2)+v2 +NP3", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "Sentence (12) NP1+v1+NP2 is a prototype SD, this SD is the subject of v2. Figure 3 : The process of NP allocated ambiguity in garden-path sentence. Where ( ) is the flag indicating that the content in it is prototype SD.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 74, |
| "end": 82, |
| "text": "Figure 3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Main verb is NP2 is", |
| "sec_num": null |
| }, |
| { |
| "text": "To conduct a reliable evaluation, a test sentence set and a simple knowledge base were developed. The test set includes 100 manually annotated Chinese garden-path sentences and 100 non garden-path sentences with grammatical structure NP1+v1+NP2+v2+NP3. The knowledge base includes two aspects: one is if the verb is one of VS(pro) or VO(pro), the other is the concepts which the subject/object of the verb favor. And there are about 800 verbs in our knowledge base.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "Next, two experiments have been conducted. The first one is designed to test if our model can detect garden-paths effectively. The second one is designed to evaluate if garden-path sentences can be correctly analyzed. The results of the experiments are shown in Tables 1 and 2 Where, P is precision ratio, R is recall ratio, and F is F-measure (F\u03b2=1, which is defined as 2PR/(P+R)).", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 262, |
| "end": 276, |
| "text": "Tables 1 and 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "We can see that on this small test set, our algorithm achieves good performance in detection and analysis of Chinese garden-path sentences. We also conducted an error analysis, showing that two main factors lead to detection errors.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "The first is that attributer processing of NP2' s is not considered. For example, in \u7ba1\u7406\u670b\u53cb\u7684 \u516c\u53f8(manage the friend' s company), \u670b\u53cb\u7684\u516c \u53f8(the friend' s company) is an NP which cannot be divided to NP21(\u670b\u53cb friend)+\u7684+NP22(\u516c \u53f8 company) and cannot be detected if there is an SD type ambiguity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "The second is coordination ambiguity interacting with NP allocated ambiguity, as in Sandra bumped into the busboy and the waiter told her to be careful, which has not been considered.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "Furthermore, there are two sentences correctly detected as Chinese garden-path sentences, but there are neither SD type ambiguities nor NP allocated ambiguities in them. This is why there are 79 correct detections in Table 1 , but only 77 correct analyses in Table 2 . One of these sentences is sentence (17), in which \u6211\u662f\u53bf\u957f(I am the mayor) looks like a prototype SD, however \" \u662f\u2026\u7684\"is used to emphasized \u53bf\u957f(the mayor) in Chinese.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 217, |
| "end": 224, |
| "text": "Table 1", |
| "ref_id": null |
| }, |
| { |
| "start": 259, |
| "end": 266, |
| "text": "Table 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "(17) \u6211\u662f\u53bf\u957f\u6d3e\u6765\u7684(It is the mayor that instructed me to come here)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation and Discussion", |
| "sec_num": "6." |
| }, |
| { |
| "text": "The contributions of this paper are three-fold.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "First, the Sentence Degeneration model is introduced which can represent the differences in interpretation of the same grammatical structure.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "Second, we represent garden-paths as SD type ambiguity and NP allocated ambiguity. These two ambiguities come from semantics but not grammar.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "Third, we present a unified approach to processing garden-paths, in which ambiguity detection and analysis take the place of revision. The result of our approach is the semantic structure of a garden-path sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "The results of two experiments we conducted show that our model and algorithm can analyze Chinese garden-path sentences effectively. In our future work, we will build a complex knowledge base for verbs to support our semantic analysis. We will also develop attributer processing and coordination disambiguation to improve the performance of our algorithm.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "Moreover, we will extend our algorithm to detect and analyze garden-paths caused by sentences which have no verbs. This phenomenon is a typical ambiguity in Chinese sentences, such as. Sentence (18):", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "(18) \u5979\u7a7f\u88d9\u5b50\u5f88\u6f02\u4eae(She is beautiful dressing skirt).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Work", |
| "sec_num": "7." |
| }, |
| { |
| "text": "This NP has to be translated as an attributive clause using which in English.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "I thank Prof. Zengyang Huang and Dr. Chuanjiang Miao for their valuable comments on an early draft of this paper. I also thank Zheng Wu for his wonderful work in algorithm development.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgments", |
| "sec_num": null |
| } |
| ], |
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