• Journal of Digital Convergence
• /
• v.13 no.3
• /
• pp.295-304
• /
• 2015

We assessed reading-related skills in children with reading comprehension difficulties ("Poor comprehenders"), word decoding difficulties ("Poor decoders") and normal readers, matched for age and nonverbal IQ. The reading-related skill tests used in our study are phonological processing, visual processing test, and receptive vocabulary test. The authors argue that children who had difficulty in reading comprehension had lower scores only on the phonological short-term memory test compared with normal readers, although their performance on receptive vocabulary and visual processing tests are comparable to normal readers. The results of our study revealed that poor decoders had lower scores on the phonological processing, visual processing, and receptive vocabulary tests.

• Journal of Korean Library and Information Science Society
• /
• v.52 no.3
• /
• pp.197-215
• /
• 2021

This study analyzed experimental studies of intervention programs provided to improve reading comprehension of elementary school students using a systematic review research method. Cochrane's handbook was applied, and key questions were formulated according to the PICOTS-SD. The inclusion and exclusion criteria were applied to 1,610 papers searched in KCI, and the final 18 cases were selected for analysis. The risk of bias was evaluated using the 'RevMan5' tool provided by Cochrane. As a result of the evaluation, the risk of bias was low in most areas. And for the qualitative analysis of these papers, a data summary table was prepared. As a result, the types of intervention programs were diverse, and in many cases, upper grade students were applied. The size of the experimental group ranged from 6 to 29, and there were more QED-based studies than RCT-based studies. The most mediation time was 40 minutes, and the number of mediation sessions was 8 to 24. And in all studies, the intervention programs were found to be effective in improving reading comprehension.

• Proceedings of the KSLP Conference
• /
• 2003.11a
• /
• pp.148-148
• /
• 2003

목적 : 베르니케실어증환자의 치료에 있어서 전통적 기법은 청각적 이해력을 높이기 위해 청각변별력을 직접적으로 치료한다. 그에 반해 Helm-estabrooks 등(1991)에 의해 개발된 TWA(Treatment for Wernicke's Aphasia)는 읽고 이해하기(reading comprehension), 구두읽기(oral reading), 따라 말하기(repetition)등의 과정을 통해 재청각화(reauditorization)를 유발하여 청각이해력(auditory comprehension)을 향상시키는 언어치료 프로그램이다. 본 연구자들은 TWA가 베르니케실어증을 가진 한국인의 재청각화에도 효과적인지를 알아보고자 하였다. (중략)

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.411-418
• /
• 2009

The purpose of this study is to research the effects of English reading strategies on English reading comprehension by Korean college students. Reading strategy use was assessed through Oxford's self-report questionnaire in reading strategies. This study has three research questions. The first question was to investigate some reading strategies used by college students. The second question was to investigate the differences in reading strategies between two groups in gender. The third question was to investigate the differences in reading strategies of three college student groups according to their English proficiency estimated by reading scores. Some major findings of this study are as follows. First, college English learners use memory strategies most frequently of the six strategies, while using metacognitive strategies least frequently. Second, there exists a significant difference in reading strategies between the gender group. Third, there also exists a significant difference in reading strategies among the three groups divided according to English proficiency. This study shows that students' reading ability can be strengthened and motivated by some reading strategies in reading practice. It also means that it is necessary for English teachers to take into consideration the reading strategies suitable for the students in their reading classes.

• Smart Media Journal
• /
• v.11 no.10
• /
• pp.89-96
• /
• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.4
• /
• pp.145-152
• /
• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

• Journal of Internet of Things and Convergence
• /
• v.8 no.5
• /
• pp.17-25
• /
• 2022

Recently the massive amount of data has been generated because of the number of edge devices increases. And especially, the number of raw unstructured HTML documents has been increased. Therefore, MRC(Machine Reading Comprehension) in which a natural language processing model finds the important information within an HTML document is becoming more important. In this paper, we propose HTDE(HTML Tag Depth Embedding Method), which allows the BERT to train the depth of the HTML document structure. HTDE makes a tag stack from the HTML document for each input token in the BERT and then extracts the depth information. After that, we add a HTML embedding layer that takes the depth of the token as input to the step of input embedding of BERT. Since tokenization using HTDE identifies the HTML document structures through the relationship of surrounding tokens, HTDE improves the accuracy of BERT for HTML documents. Finally, we demonstrated that the proposed idea showing the higher accuracy compared than the accuracy using the conventional embedding of BERT.

• English Language & Literature Teaching
• /
• v.7 no.1
• /
• pp.137-160
• /
• 2001

This paper is to suggest the way to improve EFL reading skill through semantic mapping by leveled group activities. Semantic mapping is a categorical structuring of information in graphic forms or diagrams. It can be used to activate and organize background knowledge on topics in classrooms. For small group activities, the class is divided into higher leveled groups and lower leveled groups of four members based on their grades. The teaching process has three stages: Pre-reading, while-reading, and post-reading. In the pre-reading stage, students discuss what they know about the topic. They map ideas with a brainstorming technique. In the while-reading stage, they read the text about the topic. While they are reading, they could ask some questions they might have and discuss the information in the text and categorize them with semantic mapping. In the post-reading stage, they discuss what they thought of the topic and add some information about the topic with semantic mapping. For the subjects of this study, third grade, middle school students were selected: 41 students for the experimental group and 35 students for the control group. The experimental period covered almost one semester from March to August, 2000. The results were as follows: 1) The students in the experimental group had higher scores in reading comprehension than those in the control group when semantic mapping was used; 2) The use of semantic mapping in reading comprehension was found to be much more effective in the higher leveled group than in the lower leveled group; 3) The results of questionnaires showed that many students became more interested and motivated in English, and semantic mapping helped them to participate positively in reading the English text. Thus, using semantic mapping by leveled group activities can be an effective alternative to traditional teaching methods for teachers who desire to improve reading skill in middle school students' English classes.

• Journal of information and communication convergence engineering
• /
• v.7 no.4
• /
• pp.419-430
• /
• 2009

Reading comprehension (RC) systems are to understand a given text and return answers in response to questions about the text. Many previous studies extract sentences that are the most similar to questions as answers. However, texts for RC tests are generally short and facts about an event or entity are often expressed in multiple sentences. The answers for some questions might be indirectly presented in the sentences having few overlapping words with the questions. This paper proposes a conceptual graph matching method towards RC tests to extract answer strings. The method first represents the text and questions as conceptual graphs, and then extracts subgraphs for every candidate answer concept from the text graph. All candidate answer concepts will be scored and ranked according to the matching similarity between their sub-graphs and question graph. The top one will be returned as answer seed to form a concise answer string. Since the sub-graphs for candidate answer concepts are not restricted to only covering a single sentence, our approach improved the performance of answer extraction on the Remedia test data.

• Korean Journal of Child Studies
• /
• v.30 no.3
• /
• pp.23-36
• /
• 2009

Relationships between immigrant mothers' Korean language ability and preschoolers' language development in multi-cultural families were studied with 91 mothers and their children. Language capacity of mothers and children was measured by receptive and expressive vocabulary test, sentence comprehension test and two kinds of reading tests. Results showed that mothers' level of comprehension was relatively low but their reading ability was higher than that of elementary school first-grade Korean students. Comprehension of children in multi-cultural families with non-immigrant counterparts showed a lower level of language ability. Mother's level of expressive vocabulary, receptive vocabulary and reading ability correlated with children's language development. These results indicate a relationship between children's delayed language development in multi-cultural families and mother's low proficiency in Korean language.