Automatic Construction of the E nglish Sentence Pattern Structure Treebank for C hinese ESL learners (2024)

Abstract: “Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.”

## introduction reading comprehension is a fundamental skill in english learning, pivotal for linguistic acquisition, critical thinking, and effective communication across various contexts. for chinese esl (english as a second language) learners, the ability to analyze complicated sentences represents both a central priority and a significant challenge in reading comprehension. to overcome this reading barrier, it is essential for learners to have a certain level of grammatical knowledge. @xcite believes that grammar is very crucial for second language learners' reading ability. @xcite considers grammatical ability an important foundation for second language learners' reading, emphasizing a vital to divide sentences into correct patterns. existing analysis tutorials for complicated english sentence typically contain only hundreds of example sentences, making it difficult for students to receive immediate and targeted feedback during practice, such as a book published by new oriental education, short for noe300 @xcite . automatic syntactic analysis can compensate for this by transcending the boundaries of time and space and provide unlimited sentence analysis support. most of the current automatic english grammar parses are designed for processing simple sentences, e.g. enpuz 0 analyses sentences with an upper limit of 20 words in length. based on these considerations, this paper conduct automatic grammar analysis of long and complicated sentences without length constraints. we adopts the widely recognized sentence diagramming theory, referring its more standardized approaches such as grammar revolution 1 and sentence analytics 2 . these methods have covered most english grammatical cases, providing vivid and detailed analysis, but their perspectives of grammar explanation are not completely suited to the learning habits of chinese esl learners. therefore, we have made improvements such as grouping various clauses to-gether, focusing on adverbial accompaniment, etc, and, designed the english sentence pattern structure (esps). treebank is the processed corpus that records the syntactic annotation of every sentence, providing word segmentation, part-of-speech tagging, syntactic structure and other information. based on the proposed grammatical system, we aim to build the english sentence pattern structure treebank (espst) and further develop automatic parses. the standard methods of constructing large-scale syntactic resources are manual annotation and automatic conversion. manual annotation can ensure the data quality but is time-consuming and labor-intensive. a practical alternative method is to utilize existing treebank resources and converting them into the target treebank by finding the mapping relationships between two forms. current research on automatic treebank conversion mainly focuses on the conversion between constituency treebanks and dependency treebanks. @xcite proposed an early method using a headword node table to convert constituency trees into dependency trees. @xcite described two algorithms for converting constituency trees into dependency trees, employing a headword filtering table method, and proposed a new algorithm for converting the generated dependency trees back into constituency trees, with the results closely resembling the original penn treebank (ptb). @xcite , @xcite , and @xcite also conducted research on the conversion between constituency structure and dependency structure. in chinese, some scholars have researched the conversion between constituency treebanks, dependency treebanks, and chinese sentence pattern structure treebanks (spst). among them, zhang (2018) converted the tsinghua chinese treebank (tct) into spst, with an overall accuracy rate of 92.9%. @xcite used rule-based methods to convert the chinese treebank (ctb) into spst, with an overall accuracy rate of 89.72%. these studies have proved the feasibility of interconversion between different syntactic structures. using conversion rules and the advanced parser of the source treebank, we can automatically generate targeted trees from raw sentences. yet, creating these conversion rules is a very challenging task that needs careful observation and steady practice, posing a significant challenge to our research. considering the wide use of the ptb in natural language processing, we choose to convert the constituency structure treebank into espst. to verify the effectiveness of the conversion rules, we conducted experiments on a manually annotated test set to compare the conversion results with the effects of trained parser and large language models (llms). the results indicating that the rule-based conversion method proposed in this paper is the most effective. ## background in this paper's conversion, the source treebank is mainly the english ptb corpus, and the target treebank is the espst we designed based on sentence diagramming theory, as introduced in section 3. the formulation of conversion rules necessitates a comparative analysis of the grammatical forms between these two. this section will separately introduce ptb and sentence diagramming. ## the english sentence pattern structure based on the theories of sentence diagramming discussed in section 2.2, and in conjunction with chinese esl learners' cognitive habits, we have defined 14 grammatical labels involving sentence components and logical relationships. as shown in the ## constructing the english sentence pattern structure treebank in english, the techniques of constituency structure are relatively mature and have yielded promising results. therefore, we choose the constituency structure as the source treebank and construct the es-pst through rule-based conversion. specifically, this involves formulating conversion rules for the 14 c c l 2 0 2 4 labels explanation constituency structure conversion rules examples sbj subject @xmath1. prd predicate s/@xmath2. obj object s/@xmath3. pred predicative @xmath4. iobj indirect object s/@xmath5. ## experiments based on the rules, we completed the conversion of espst for a total of 39,406 sentences in constituency structure and manually annotated 1000 sentences as a test set to evaluate the conversion results. among the 1000 sentences, we calculated the annotation consistency to measure the rules and data quality. to further verify the effectiveness of the treebank conversion method, we set up two additional experiments: a trained parser and a llms analysis. ## conclusion we developed an esps rooted in sentence diagramming theory, which is suited to chinese esl learners. through rule-based conversion from the ptb, we constructed the espst and evaluated its effectiveness. comparative experiments, including parser training and llms analysis, showed that our treebank conversion rule-based method yielded the best results. this work provides a new perspective on efficient english grammar learning of long and complicated sentence. however, our conversion process have shortcomings, such as the rules for indirect objects, predicatives, and adverbial clauses, which need further refinement. in the future, we will continue to optimize the conversion results and build an analysis platform for esps, realizing the visualization of automatic syntactic analysis. c c l 2 0 2 4 labels explanation constituency structure conversion rules examples todo todo infinitive s/@xmath6. prep prepositional phrase @xmath7, 251.2 million shares were traded. mod modification np/@xmath8. (np/adjp/pp/s/intj/ @xmath9. @xmath10. @xmath11. @xmath12,/: + np2 + @xmath13, an analyst at lawrance inc. . @xmath14, 251.2 million shares were traded. @xmath15. @xmath16. @xmath17. advcla adverbial of accompaniment s1/@xmath18, he said october. cla clause @xmath19, even central banks can't stop it. wh relative connectives @xmath20. @xmath21. @xmath22, even central banks can't stop it. @xmath23.