{ "paper_id": "2020", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T09:40:05.922930Z" }, "title": "Using Verb Frames for Text Difficulty Assessment", "authors": [ { "first": "John", "middle": [], "last": "Lee", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Hong Kong", "location": {} }, "email": "jsylee@cityu.edu.hk" }, { "first": "Meichun", "middle": [], "last": "Liu", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Hong Kong", "location": {} }, "email": "meichliu@cityu.edu.hk" }, { "first": "Tianyuan", "middle": [], "last": "Cai", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Hong Kong", "location": {} }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.", "pdf_parse": { "paper_id": "2020", "_pdf_hash": "", "abstract": [ { "text": "This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "FrameNet (https://framenet.icsi.berkeley.edu) and other similar resources have supported a large range of natural language processing (NLP) tasks including semantic role labeling (Gildea and Jurafsky, 2002) , information extraction (Fader et al., 2011) , sentiment analysis (Ruppenhofer and Rehbein, 2012) and language learning (Carri\u00f3n, 2006; Xu and Li, 2011) . However, they have yet to be exploited for analyzing text difficulty, which is also known as readability assessment. Given any text, the system is to predict its reading difficulty, by estimating the age or school grade (e.g., Grades 1 to 13) required for readers to understand the text; by assigning it a difficulty score, such as Lexile (Stenner, 1996) ; or by locating it on a proficiency scale, such as the six-level scale in the Common European Framework of Reference for Language (2001) . Previous research on automatic readability assessment has mostly relied on lexical and syntactic features. A common lexical feature is the level of vocabulary difficulty, for example according to the number of \"difficult words\" (Kincaid et al., 1975) . Syntactic features may include parse tree patterns or, as a proxy, average sentence length. While lexical complexity and syntactic complexity have been shown to be effective predictors of text readability, they do not capture all aspects of reading difficulty. Consider the pairs of example sentences in Table 1 . The sentences in each pair have comparable vocabulary difficulty and sentence length. Sentences (1a) and (1b) both have the verb 'worry'. The verb in (1a) takes as object a short clause 'you would get sick', but in (1b) it takes an abstract noun, 'your health', which may be more difficult to process. Likewise, sentences (2a) and (2b) are semantically similar, but the reason construction 'because [he] missed the exam' in the latter may make it harder to read than the former. Finally, sentence (3b) is likely more challenging to understand than (3a) due to a metaphorical usage. Semantic analysis can be expected to improve the readability assessment for such sentences. While some existing assessment models already incorporate semantic features, they are mostly limited to anaphora patterns, word senses and semantic categories of individual words (Pil\u00e1n et al., 2014; Sung et al., 2015; Schumacher et al., 2016) . Salient features may potentially be derived from semantic frames, such as those in FrameNet, Chinese Framenet (You and Liu, 2005) , or Mandarin VerbNet (Liu, 2016; Liu and Chang, 2016; Liu, 2018; Liu, 2019) . Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, this paper investigates the correlation between verb frames and text difficulty.", "cite_spans": [ { "start": 179, "end": 206, "text": "(Gildea and Jurafsky, 2002)", "ref_id": "BIBREF7" }, { "start": 232, "end": 252, "text": "(Fader et al., 2011)", "ref_id": "BIBREF4" }, { "start": 274, "end": 305, "text": "(Ruppenhofer and Rehbein, 2012)", "ref_id": "BIBREF23" }, { "start": 328, "end": 343, "text": "(Carri\u00f3n, 2006;", "ref_id": "BIBREF0" }, { "start": 344, "end": 360, "text": "Xu and Li, 2011)", "ref_id": "BIBREF30" }, { "start": 702, "end": 717, "text": "(Stenner, 1996)", "ref_id": "BIBREF25" }, { "start": 849, "end": 855, "text": "(2001)", "ref_id": null }, { "start": 1086, "end": 1108, "text": "(Kincaid et al., 1975)", "ref_id": "BIBREF13" }, { "start": 2278, "end": 2298, "text": "(Pil\u00e1n et al., 2014;", "ref_id": "BIBREF22" }, { "start": 2299, "end": 2317, "text": "Sung et al., 2015;", "ref_id": "BIBREF26" }, { "start": 2318, "end": 2342, "text": "Schumacher et al., 2016)", "ref_id": "BIBREF24" }, { "start": 2455, "end": 2474, "text": "(You and Liu, 2005)", "ref_id": "BIBREF31" }, { "start": 2497, "end": 2508, "text": "(Liu, 2016;", "ref_id": "BIBREF18" }, { "start": 2509, "end": 2529, "text": "Liu and Chang, 2016;", "ref_id": "BIBREF15" }, { "start": 2530, "end": 2540, "text": "Liu, 2018;", "ref_id": "BIBREF19" }, { "start": 2541, "end": 2551, "text": "Liu, 2019)", "ref_id": "BIBREF20" } ], "ref_spans": [ { "start": 1415, "end": 1422, "text": "Table 1", "ref_id": null } ], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "We hypothesize that the verb usage patterns encoded in verb frames can be associated with different levels of reading difficulty. The distribution of frame-related attributes in a text may therefore be correlated with readability. More precisely, this paper tests the following hypotheses:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "\u2022 H1: Non-core frame elements are more frequently used in more difficult texts (Section 5);", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "\u2022 H2: Core frame elements are more frequently omitted in more difficult texts (Section 6);", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "\u2022 H3: For verbs that can take either a noun phrase (NP) or a clause as argument, NPs are more frequently chosen in more difficult texts (Section 7).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "\u2022 H4: Metaphor is more frequently used in more difficult texts (Section 8).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "The rest of this paper is organized as follows. After a summary of previous research on readability assessment (Section 3), we describe our dataset (Section 4). We then present results on the four hypotheses above (Sections 5 to 8).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Research Questions", "sec_num": "2." }, { "text": "This section reviews the variety of lexical, syntactic and semantic features that have been explored for readability assessment.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Previous Work", "sec_num": "3." }, { "text": "Most readability formulas rely on shallow features such as word length, sentence length, and vocabulary lists (Kincaid et al., 1975) . The Lexile framework incorporates features derived from word frequencies, for instance lexical richness based on the type-token ratio (Stenner, 1996) . More difficult Metaphorical usage with 'put' 'He did not care about (lit., 'put on heart') this question' Table 1 : Sentences with varying reading difficulty due to semantic complexity, despite similar lexical and syntactic complexity.", "cite_spans": [ { "start": 110, "end": 132, "text": "(Kincaid et al., 1975)", "ref_id": "BIBREF13" }, { "start": 269, "end": 284, "text": "(Stenner, 1996)", "ref_id": "BIBREF25" } ], "ref_spans": [ { "start": 393, "end": 400, "text": "Table 1", "ref_id": null } ], "eq_spans": [], "section": "Lexical Features", "sec_num": "3.1." }, { "text": "More recent work in NLP has made use of n-gram language models (Collins-Thompson and Callan, 2004; Petersen and Ostendorf, 2009) , inflectional and derivational morphology (Hancke et al., 2012) , verbal morphology, verb tense and mood-based features (Dell'Orletta et al., 2011; Fran\u00e7ois and Fairon, 2012) . Psycholinguistic properties, such as the concreteness, imageability and meaningfulness of words (Wilson, 1988) , and the age of acquisition (Kuperman et al., 2012) , have also been shown to be helpful.", "cite_spans": [ { "start": 63, "end": 98, "text": "(Collins-Thompson and Callan, 2004;", "ref_id": "BIBREF2" }, { "start": 99, "end": 128, "text": "Petersen and Ostendorf, 2009)", "ref_id": "BIBREF21" }, { "start": 172, "end": 193, "text": "(Hancke et al., 2012)", "ref_id": "BIBREF10" }, { "start": 250, "end": 277, "text": "(Dell'Orletta et al., 2011;", "ref_id": "BIBREF3" }, { "start": 278, "end": 304, "text": "Fran\u00e7ois and Fairon, 2012)", "ref_id": "BIBREF6" }, { "start": 403, "end": 417, "text": "(Wilson, 1988)", "ref_id": "BIBREF29" }, { "start": 447, "end": 470, "text": "(Kuperman et al., 2012)", "ref_id": "BIBREF14" } ], "ref_spans": [], "eq_spans": [], "section": "Lexical Features", "sec_num": "3.1." }, { "text": "Even if a sentence is composed of simple words, it can still be difficult to understand because of complicated syntactic structure. Early models often use sentence length and clause length as proxies for syntactic complexity. More recent ones incorporate part-of-speech (POS) features, including the frequency of coordination and subordination; the nominal ratio and the pronoun/noun ratio (Pil\u00e1n et al., 2014) ; the number of different kinds of pronouns and conjunctions (Sung et al., 2015) ; and more generally, the percentage and diversity of POS tags (Vajjala and Meurers, 2014) . Parse tree depth, parse scores, subtree patterns (Heilman et al., 2008; Schumacher et al., 2016) and dependency distance (Liu, 2008) have also been found to be useful.", "cite_spans": [ { "start": 390, "end": 410, "text": "(Pil\u00e1n et al., 2014)", "ref_id": "BIBREF22" }, { "start": 472, "end": 491, "text": "(Sung et al., 2015)", "ref_id": "BIBREF26" }, { "start": 555, "end": 582, "text": "(Vajjala and Meurers, 2014)", "ref_id": "BIBREF27" }, { "start": 634, "end": 656, "text": "(Heilman et al., 2008;", "ref_id": "BIBREF11" }, { "start": 657, "end": 681, "text": "Schumacher et al., 2016)", "ref_id": "BIBREF24" }, { "start": 706, "end": 717, "text": "(Liu, 2008)", "ref_id": "BIBREF17" } ], "ref_spans": [], "eq_spans": [], "section": "Syntactic Features", "sec_num": "3.2." }, { "text": "Lexical complexity and syntactic complexity do not cover all factors that influence readability. As discussed in Section 1, the (b) sentences in Table 1 can be expected to be more difficult to read than their (a) counterparts, despite their similar lexical and syntactic complexity. Many readability models have therefore incorporated measures on semantic complexity. Common features include the average number of senses per word (Pil\u00e1n et al., 2014) ; the ratio of active/passive voice (Graesser et al., 2011) ; the number of content words and the number of semantic categories in a sentence (Sung et al., 2015) ; the number of unique entities per document and the average number of words per entity; and the semantic probability of a sentence, according to a semantic network (vor der Br\u00fcck et al., 2008).", "cite_spans": [ { "start": 430, "end": 450, "text": "(Pil\u00e1n et al., 2014)", "ref_id": "BIBREF22" }, { "start": 487, "end": 510, "text": "(Graesser et al., 2011)", "ref_id": "BIBREF9" }, { "start": 593, "end": 612, "text": "(Sung et al., 2015)", "ref_id": "BIBREF26" } ], "ref_spans": [ { "start": 145, "end": 152, "text": "Table 1", "ref_id": null } ], "eq_spans": [], "section": "Semantic Features", "sec_num": "3.3." }, { "text": "This section first presents Mandarin VerbNet and the verbs to be analyzed (Section 4.1), and then describes the corpus of graded texts on which our analysis is based (Section 4.2).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Data", "sec_num": "4." }, { "text": "Mandarin VerbNet is a verbal semantic database with annotation of frame-based constructional features (Liu and Chiang, 2008) . In addition to frame elements, its frames make use of a schema-based meaning representation and constructional patterns. Adopting a hybrid approach to the semantic analysis of the lexical-constructional behavior of Chinese verbs, it incorporates tenets of Frame Semantics (Fillmore and Atkins, 1992) and Construction Grammar (Goldberg, 1995) . We selected ten verbs from three different frame categories for this study (Table 2) . For more reliable statistics on frame distribution with respect to grade, we have deliberately chosen common verbs that are used in a wide range of grades.", "cite_spans": [ { "start": 102, "end": 124, "text": "(Liu and Chiang, 2008)", "ref_id": "BIBREF16" }, { "start": 399, "end": 426, "text": "(Fillmore and Atkins, 1992)", "ref_id": "BIBREF5" }, { "start": 452, "end": 468, "text": "(Goldberg, 1995)", "ref_id": "BIBREF8" } ], "ref_spans": [ { "start": 546, "end": 555, "text": "(Table 2)", "ref_id": "TABREF2" } ], "eq_spans": [], "section": "Mandarin VerbNet", "sec_num": "4.1." }, { "text": "We performed our analysis on a corpus of Chineselanguage textbooks constructed at Ludong University, China. 1 The 5-million-character corpus consists of more than 6000 articles, taken from 368 textbooks spanning the twelve grades in the curriculum for Chinese language in mainland China. For analysis purposes, the grades are divided into three categories:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus of Graded Text", "sec_num": "4.2." }, { "text": "\u2022 1-3: Grades 1 through 3;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus of Graded Text", "sec_num": "4.2." }, { "text": "\u2022 4-6: Grades 4 through 6;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Corpus of Graded Text", "sec_num": "4.2." }, { "text": "\u2022 7+: Grades 7 through 12. Table 2 shows the number of sentences in which the ten verbs appear. We manually and exhaustively annotated the verb frame usage in these sentences.", "cite_spans": [], "ref_spans": [ { "start": 27, "end": 34, "text": "Table 2", "ref_id": "TABREF2" } ], "eq_spans": [], "section": "Corpus of Graded Text", "sec_num": "4.2." }, { "text": "Similar to FrameNet, Mandarin VerbNet distinguishes between \"core\" or \"non-core\" frame elements. Core frame Table 3 : Verbs and their frame elements, showing the lowest grade in which the frame element appears. elements are fundamental; they commonly appear as a necessary argument in a sentence and plays an essential role in the event frame. Non-core frame elements are optional; they are \"potentially relevant\", and can be added to a sentence as an adjunct (Liu and Chiang, 2008) .", "cite_spans": [ { "start": 460, "end": 482, "text": "(Liu and Chiang, 2008)", "ref_id": "BIBREF16" } ], "ref_spans": [ { "start": 108, "end": 115, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Use of Non-core Frame Elements", "sec_num": "5." }, { "text": "According to the first hypothesis (H1), non-core frame elements are used more frequently in more difficult text. As a preliminary investigation, we identified the lowest grade at which a frame element occurs. As shown in Table 3 , many non-core frame elements are found only at higher grades. For x\u012by\u01d0n 'attract', for example, Result and Reason do not appear until Grade 7.", "cite_spans": [], "ref_spans": [ { "start": 221, "end": 228, "text": "Table 3", "ref_id": null } ], "eq_spans": [], "section": "Use of Non-core Frame Elements", "sec_num": "5." }, { "text": "To test H1, we calculated the percentage of sentences with non-core frame elements at each grade. The verb zh\u00f9y\u00ecd\u00e0o 'notice' does not employ non-core frame elements at any grade level in our dataset. As shown in statistics of the remaining verbs lend support to the hypothesis. Eight of the verbs 2 exhibit a lower percentage of sentences with non-core frame elements at grades 1-3 than at higher grades. Consider g\u01cend\u00f2ng 'be moved' as an example: non-core frame elements for this verb 3 do not appear in grades 1-3, but account for 55.0% of the sentences in grades 4-6 and 40.0% in higher grades. The difference between grades 4-6 and 7+, however, is less clear-cut. Five of the verbs exhibit higher rates of non-core frame elements in grades 7+, while four exhibit lower rates. More finegrained analysis is necessary to account for the underlying differences.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Use of Non-core Frame Elements", "sec_num": "5." }, { "text": "To reduce repetition, a writer may omit a verb argument from a sentence, expecting the reader to infer the information from the context. This phenomenon is frequent in Chinese even for some core arguments; for example, prodropped subjects account for more than 36% of the subjects in Chinese sentences (Kim, 2000) . The number of zero pronouns is likely correlated with the effort needed for resolution. According to the second hypothesis (H2), omission of core frame elements is more frequent in more difficult texts.", "cite_spans": [ { "start": 302, "end": 313, "text": "(Kim, 2000)", "ref_id": "BIBREF12" } ], "ref_spans": [], "eq_spans": [], "section": "Omission of Core Frame Elements", "sec_num": "6." }, { "text": "We first examine frame elements that normally occupy the subject position before the verb. Table 5 shows the proportion of sentences containing these frame elements. 4 For the verbs g\u01cend\u00f2ng 'be moved' and x\u012by\u01d0n 'attract', this proportion is constant since all of their sentences at all grades contain subjects. The hypothesis is however supported by the remaining eight verbs. Generally, more sentences lack subjects in the higher grades than in the lower ones. The gap between grades 4-6 and 7+ is usually larger than the 14.3% 23.3% 32.5% zh\u0101oj\u00ed 'be anxious' 0% 10.7% 16.7% Table 6 : (H2) on objects: Percentage of sentences with frame elements serving as the direct object of the verb.", "cite_spans": [ { "start": 166, "end": 167, "text": "4", "ref_id": null } ], "ref_spans": [ { "start": 91, "end": 98, "text": "Table 5", "ref_id": "TABREF6" }, { "start": 576, "end": 583, "text": "Table 6", "ref_id": null } ], "eq_spans": [], "section": "Subjects", "sec_num": "6.1." }, { "text": "gap between 1-3 and 4-6. 5", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Subjects", "sec_num": "6.1." }, { "text": "We next investigate frame elements that normally occupy the object position after the verb. Table 6 shows the proportion of sentences containing these frame elements. 6 Two of the verbs, f\u00e0ng 'put' and di\u016b 'cast away', always have explicit objects in sentences at all grades, as the frame element Figure is indispensable for their semantic expression. Among the remaining eight verbs, the trend is more nuanced compared to the omission of subjects. We will focus on comparing grades 1-3 with the higher grades. Consistent with H2, four of these verbs -f\u0101xi\u00e0n 'discover', zh\u00f9y\u00ecd\u00e0o 'notice', g\u01cend\u00f2ng 'be moved' and d\u0101nx\u012bn 'worry' -have more sentences in grades 1-3 containing objects. In contrast, for the other four verbs, the sentences in grades 1-3 are more likely to omit the object. These results sug- gest that the impact of the object on text difficulty differs according to usage patterns of individual verbs. The presence of objects in the verbs zh\u0101oj\u00ed 'be anxious' and h\u00f2uhu\u01d0 'regret', for instance, can make a sentence harder to read. Since these verbs do not take objects in most instances, the absence of the object should perhaps not be considered an omission.", "cite_spans": [], "ref_spans": [ { "start": 92, "end": 99, "text": "Table 6", "ref_id": null } ], "eq_spans": [], "section": "Objects", "sec_num": "6.2." }, { "text": "As illustrated by the verb d\u0101nx\u012bn 'worry' in sentences 1aand (1b) in Table 1 , some verb arguments can be either a noun phrase (NP) or a clause. The distinction is reflected by the frame element. Sentence (1a), which contains the clause 'you would get sick' as object, has the frame element Target-possible-situation. In contrast, sentence (1b), with the NP 'your health' as object, has the Target-entity element. Similar distinctions are made in other frame categories, for example with Phenomenon (clause) vs. Topic (NP), and Given-fact (clause) vs. Given-fact-description (NP). According to the third hypothesis (H3), given a choice between NP and clause for an eventive complement, NP or event nominal is more often used in difficult texts than in easier ones. We analyzed the four verbs in our dataset that offer this choice, and the overall statistics support the hypothesis (Table 7) . For all four verbs, sentences in grades 1-3 substantially prefer clause over NP, and the gap narrows in grades 7+; in the case of zh\u00f9y\u00ecd\u00e0o 'notice', clauses are even outnumbered by NPs in grades 7+. This observation suggests that for these verbs, a clause may be easier for less proficient readers to understand than a noun, especially when it expresses an abstract meaning. When taking grades 4-6 into account, the statistics are not always consistent with H3. Consider the case of f\u0101xi\u00e0n 'discover'. While the preference for clause over NP decreases from grades 1-3 (a difference of 56.8%) to grades 4-6 (a difference of 5.9%), it unexpectedly increases again from grades 4-6 to grades 7+ (a difference of 11.1%).", "cite_spans": [], "ref_spans": [ { "start": 69, "end": 76, "text": "Table 1", "ref_id": null }, { "start": 881, "end": 890, "text": "(Table 7)", "ref_id": "TABREF8" } ], "eq_spans": [], "section": "Clause vs. Noun Phrase", "sec_num": "7." }, { "text": "Metaphorical usage which involves cognitive transfer from one domain to another tends to make a sentence harder to read, even when the vocabulary and syntactic structures are simple. Consider the example sentences (3a) and (3b) in Verb Grades 1-3 4-6 7+ f\u00e0ng 'put' 0.0% 19.2% 30.60% di\u016b 'cast away' 0.0% 11.8% 33.30% Table 8 : (H4) Percentage of sentences with metaphoric usage. Table 1 . In (3a), the verb \u653e f\u00e0ng 'put' is used in its regular sense, 'put a book on the table'. In (3b), however, it is used in the metaphorical sense in the verb phrase \u653e\u5728\u5fc3\u4e0a ('remember'; literally, \"put on the heart\"), which is more difficult to interpret. Our analysis centered on the two verbs in our datasetf\u00e0ng 'put' and di\u016b 'cast away' -that are more productive in metaphorical usage. In non-metaphorical usage, the frame elements Ground-Location (for f\u00e0ng 'put') and Figure ( for di\u016b 'cast away') typically expect physical locations and objects. That is not necessarily the case in metaphorical usage, which allows abstract entities such as 'worry' (e.g., \"cast away one's worry\") or 'heart' (\"put on the heart\"). The fourth hypothesis (H4) predicts metaphorical usage to be more frequent in more difficult texts. Table 8 presents evidence for this hypothesis. For both verbs, no metaphor is employed in the texts for grades 1-3. The percentage of metaphorical usage increases to 19.2% and 11.8%, respectively, at grades 4-6. The higher grades see even more substantial amount of metaphorical usage, at 30.60% and 33.30%.", "cite_spans": [], "ref_spans": [ { "start": 317, "end": 324, "text": "Table 8", "ref_id": null }, { "start": 379, "end": 386, "text": "Table 1", "ref_id": null }, { "start": 855, "end": 863, "text": "Figure (", "ref_id": null }, { "start": 1202, "end": 1209, "text": "Table 8", "ref_id": null } ], "eq_spans": [], "section": "Metaphor", "sec_num": "8." }, { "text": "We have presented the first investigation on the correlation between verb frames and text difficulty. Based on Mandarin VerbNet (Liu, 2016; Liu and Chang, 2016; Liu, 2018; Liu, 2019) , our analysis of ten common Chinese verbs showed that at higher grades, there is generally more frequent use of non-core frame elements; more frequent omission of core frame elements that normally occupy the subject position before the verb; increased preference for a noun phrase over a clause as verb argument; and more frequent metaphorical usage. These patterns can potentially help improve a readability assessment model. We plan to pursue two directions in future work. First, we plan to expand our analysis to a larger set of verbs from diverse frame categories. Second, we intend to incorporate frame patterns as features in a system for readability prediction.", "cite_spans": [ { "start": 128, "end": 139, "text": "(Liu, 2016;", "ref_id": "BIBREF18" }, { "start": 140, "end": 160, "text": "Liu and Chang, 2016;", "ref_id": "BIBREF15" }, { "start": 161, "end": 171, "text": "Liu, 2018;", "ref_id": "BIBREF19" }, { "start": 172, "end": 182, "text": "Liu, 2019)", "ref_id": "BIBREF20" } ], "ref_spans": [], "eq_spans": [], "section": "Conclusions", "sec_num": "9." }, { "text": "We thank Prof. Xu Dekuan for providing access to this corpus.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "With the exception of h\u00f2uhu\u01d0 'regret'.3 SeeTable 3for example sentences for the non-core frame elements Result and Means.4 Among the ten verbs analyzed, depending on their frame category, these frame elements can be Agent, Cognizer, Exp, Placer, Affector or Affectee.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "For the verb d\u0101nx\u012bn 'worry', for example, all sentences in grades 1-3 have subjects, as do 95.8% of the sentences in grades 4-6. However, the figure drops to 71.4% at grades 7+. The only exception to this trend is observed for zh\u0101oj\u00ed 'be anxious'.6 Among the ten verbs analyzed, depending on their frame category, these frame elements can be Affectee,Figure,Given Fact, Given Fact Description, Phenomenon, Topic, Target, Target Empathy, Target-Entity, Target-Situation or Target-Possible-Situation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "We thank Prof. Dekuan Xu for providing access to the corpus of Chinese-language textbooks, and Tianqi He and Yingying Ye for their assistance. We gratefully acknowledge support from a grant (Project #9360163) from the Hong Kong Institute for Data Science at City University of Hong Kong.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Acknowledgements", "sec_num": "10." } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Framenet as a Corpus Tool for the Learning of Second Languages and for the Lexical Awareness of one's First Language", "authors": [ { "first": "O", "middle": [ "B" ], "last": "Carri\u00f3n", "suffix": "" } ], "year": 2006, "venue": "Porta Linguarum", "volume": "6", "issue": "", "pages": "67--76", "other_ids": {}, "num": null, "urls": [], "raw_text": "Carri\u00f3n, O. B. (2006). Framenet as a Corpus Tool for the Learning of Second Languages and for the Lexical Awareness of one's First Language. 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VerbFrame Element Lowest TypeSelected examples
d\u0101nx\u012bnExp1Core\u6211 Exp \u62c5 \u62c5 \u62c5\u5fc3 \u5fc3 \u5fc3 \u6211\u4f1a\u751f\u75c5 Target-possible-situation
'worry'Target-Possible-1CoreI Exp worried I would get sick Target-possible-situation
Situation
Beneficiary2Non-core \u6211 Exp \u62c5 \u62c5 \u62c5\u5fc3 \u5fc3 \u5fc3 \u4f60\u7684\u5065\u5eb7 Target-entity
Target Entity4CoreI Exp worried about your health Target-entity
Stim7Non-core
g\u01cend\u00f2ngAffector2Core\u89c2\u4f17 .
Affector2Core
Means Non-core \u4ed6 Affectee Theme 6 4 Core He Affector so attracted the kids Affectee , making them think
Result7Non-core of him Result ...
Reason7Non-core
f\u0101xi\u00e0nCognizer1Core\u6211 Cognizer \u5728\u684c\u4e0a Medium \u53d1 \u53d1 \u53d1\u73b0 \u73b0 \u73b0 \u4e00\u672c\u4e66 Topic
'discover'Phenomenon1CoreI Cognizer found a book Topic on the table Medium
Means1Non-core
Topic2Core\u4ece \u7784\u51c6\u955c Instrument \u91cc, \u53d1 \u53d1 \u53d1\u73b0 \u73b0 \u73b0 \u4e00\u4e2a\u706b\u7403\u7a7f\u8fc7 Phenomenon ...
Medium2Non-core From the telescope Instrument , [we] discovered a fireball
Instrument7Non-core shooting Phenomenon ...
h\u00f2uhu\u01d0Exp2Core\u5982\u679c \u4e0d\u590d\u4e60 Stim \u4ed6 Exp \u4f1a\u540e \u540e \u540e\u6094 \u6094 \u6094\u7684
'regret'Expressor2Non-Core He Exp would regret if [he] didn't review Stim
Given-fact3Core
Reason6Non-Core \u4ed6 Affector \u56e0\u4e3a\u9519\u8fc7\u4e86\u8003\u8bd5 Reason \u5341\u5206\u540e \u540e \u540e\u6094 \u6094 \u6094
Stim7CoreBecause of missing the exam Reason , he Exp felt regretful.
Given-fact-10Core
description
" }, "TABREF3": { "html": null, "text": "", "type_str": "table", "num": null, "content": "
, the overall
" }, "TABREF4": { "html": null, "text": "", "type_str": "table", "num": null, "content": "
: (H1) Percentage of sentences with non-core frame
elements.
" }, "TABREF6": { "html": null, "text": "", "type_str": "table", "num": null, "content": "
: (H2) on subjects: Percentage of sentences with
frame elements serving as the subject of the verb.
VerbGrades
1-34-67+
f\u00e0ng 'put'100%100%100%
di\u016b 'cast away'100%100%100%
f\u0101xi\u00e0n 'discover'100% 100% 99.4%
zh\u00f9y\u00ecd\u00e0o 'notice'100% 94.4% 98.2%
g\u01cend\u00f2ng 'be moved' 100% 70.0% 86.7%
x\u012by\u01d0n 'attract'94.7% 90.0% 98.0%
d\u0101nx\u012bn 'worry'75%66.7% 67.6%
h\u00f2uhu\u01d0 'regret'44.0% 66.7% 66.7%
s\u012bk\u01ceo 'reflect'
" }, "TABREF8": { "html": null, "text": "", "type_str": "table", "num": null, "content": "
: (H3) Percentage of sentences with clause or noun
phrase as argument to the verb.
" } } } }