Machine learning approach of Japanese composition scoring and writing aided system's design

• URL: http://arxiv.org/abs/2008.11488v1
• Date: Wed, 26 Aug 2020 11:01:13 GMT
• Title: Machine learning approach of Japanese composition scoring and writing aided system's design
• Authors: Wanhong Huang
• Abstract summary: A composition scoring system can greatly assist language learners. It can make language leaner improve themselves in the process of output something. Especially for foreign language learners, lexical and syntactic content are usually what they are more concerned about.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic scoring system is extremely complex for any language. Because natural language itself is a complex model. When we evaluate articles generated by natural language, we need to view the articles from many dimensions such as word features, grammatical features, semantic features, text structure and so on. Even human beings sometimes can't accurately grade a composition because different people have different opinions about the same article. But a composition scoring system can greatly assist language learners. It can make language leaner improve themselves in the process of output something. Though it is still difficult for machines to directly evaluate a composition at the semantic and pragmatic levels, especially for Japanese, Chinese and other language in high context cultures, we can make machine evaluate a passage in word and grammar levels, which can as an assistance of composition rater or language learner. Especially for foreign language learners, lexical and syntactic content are usually what they are more concerned about. In our experiments, we did the follows works: 1) We use word segmentation tools and dictionaries to achieve word segmentation of an article, and extract word features, as well as generate a words' complexity feature of an article. And Bow technique are used to extract the theme features. 2) We designed a Turing-complete automata model and create 300+ automatons for the grammars that appear in the JLPT examination. And extract grammars features by using these automatons. 3) We propose a statistical approach for scoring a specify theme of composition, the final score will depend on all the writings that submitted to the system. 4) We design an grammar hint function for language leaner, so that they can know currently what grammars they can use.

Related papers

• Learning Language Structures through Grounding [8.437466837766895]
  We consider a family of machine learning tasks that aim to learn language structures through grounding. In Part I, we consider learning syntactic parses through visual grounding. In Part II, we propose two execution-aware methods to map sentences into corresponding semantic structures. In Part III, we propose methods that learn language structures from annotations in other languages.
  arXiv  Detail & Related papers  (2024-06-14T02:21:53Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• Identifying concept libraries from language about object structure [56.83719358616503]
  We leverage natural language descriptions for a diverse set of 2K procedurally generated objects to identify the parts people use. We formalize our problem as search over a space of program libraries that contain different part concepts. By combining naturalistic language at scale with structured program representations, we discover a fundamental information-theoretic tradeoff governing the part concepts people name.
  arXiv  Detail & Related papers  (2022-05-11T17:49:25Z)
• AUTOLEX: An Automatic Framework for Linguistic Exploration [93.89709486642666]
  We propose an automatic framework that aims to ease linguists' discovery and extraction of concise descriptions of linguistic phenomena. Specifically, we apply this framework to extract descriptions for three phenomena: morphological agreement, case marking, and word order. We evaluate the descriptions with the help of language experts and propose a method for automated evaluation when human evaluation is infeasible.
  arXiv  Detail & Related papers  (2022-03-25T20:37:30Z)
• VLGrammar: Grounded Grammar Induction of Vision and Language [86.88273769411428]
  We study grounded grammar induction of vision and language in a joint learning framework. We present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously.
  arXiv  Detail & Related papers  (2021-03-24T04:05:08Z)
• Challenges Encountered in Turkish Natural Language Processing Studies [1.52292571922932]
  Natural language processing is a branch of computer science that combines artificial intelligence with linguistics. In this study, the interesting features of Turkish in terms of natural language processing are mentioned.
  arXiv  Detail & Related papers  (2021-01-21T08:30:33Z)
• Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale [52.663117551150954]
  A few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models.
  arXiv  Detail & Related papers  (2020-10-26T13:57:20Z)
• Automatic Extraction of Rules Governing Morphological Agreement [103.78033184221373]
  We develop an automated framework for extracting a first-pass grammatical specification from raw text. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world's languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results.
  arXiv  Detail & Related papers  (2020-10-02T18:31:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.