ドイツ語学習者の語彙力と第一外国語としての英語の影響

本稿は、ドイツ語初学者を対象に行ったドイツ語習得語彙に関する調査結果を分析し、その問題点と今後の課題について報告する。語彙力に関する調査は、1 年間ドイツ語を週 2 コマ受講し、2 年目も継続してドイツ語を履修している 22 名の学生に行った。調査の結果、特に日本語からドイツ語へ翻訳する際に第一外国語として学習した英語の影響を少なからず受けていることがわかった。英語による影響と干渉は、個人差があるものの、ドイツ語学習者、特に初学者においてドイツ語の語彙が定着していないがゆえに現れた結果とも言える。それゆえにドイツ語初学者には英語との類似性や差異を意識させつつ、ドイツ語の語彙を定着させていくことが重要である

Differentiation of hepatic tumors by use of image contrast with T2-weighted MRI

金沢大学医薬保健研究域Differentiation of hepatic tumors is often evaluated in terms of qualitative diagnostic performance. The signal intensity patterns of hepatic masses are known to differ on certain T2-weighted imaging sequences. In this study, we investigated the quantitative analysis of hepatic masses by using an index called the "T2-shine ratio." Fast-spin-echo (FSE), half-Fourier acquisition single-shot turbo spin echo (HASTE), and true-FISP sequences obtained with quick-imaging techniques during a single breath-hold were examined in 74 patients. T2-shine ratios were calculated by use of the signals of regions of interest (ROIs) placed on a tumor and peripheral tissue: the T2-shine ratio is defined as (tumor signal-liver signal)/liver signal. The rate of change in the T2-shine ratio was compared among three sequences of FSE, HASTE, and true-FISP. The T2-shine ratio of FSE deducted from HASTE was significantly higher for hepatic cysts than for other masses. The T2-shine ratio of HASTE deducted from True-FISP was less than zero for hemangioma. For the value that deducted the T2-shine ratio of HASTE from the T2-shine ratio of true-FISP, hemangiomas had a significantly lower value than did cysts and metastases (P < 0.05), but there was no significant difference from hepatocellular carcinomas (HCCs). Although liver cysts, cavernous hemangiomas, and other lesions could be differentiated, it was virtually impossible to distinguish HCCs from metastatic tumors. In conclusion, the quantitative analysis of hepatic tumors was able to differentiate among these lesions by use of the T2-shine ratio. © 2008 Japanese Society of Radiological Technology and Japan Society of Medical Physics

Project Report: Development of an Effective Support System for a Foreign Language Proficiency Training Program (in Languages Other than English) for Students Studying Abroad

This article reports the results of a three-year education project (from 2015 to 2017) conducted by the Faculty of Foreign Language Studies of the Institute of Liberal Arts and Science. This project aimed developed an effective education system inside and outside of classes for students preparing to study abroad or continuing learning after coming back from studying abroad. According to the analysis of the investigation into the students’ actual learning requirements, we consider the further possibilities of learning support provided by teachers

ラジオ放送の文学番組からみるオーストリア・ファシズムの文化政策とその影響

金沢大学国際基幹教育院外国語教育系本研究は，1930年代ウィーンのラジオ放送の文学番組をラジオ雑誌や新聞等を手がかりに，オーストリア・ファシズム（1934-1938）期の文化政策とその影響の分析と考察を試みた。研究期間内においては，研究課題に関する文献収集と整理という基礎研究を進めるとともに，文学番組で扱われた個々の作品および当該時代を生きた作家ジョルジュ・ザイコ（1892-1962）の作品を分析した。本課題の研究と本研究を踏まえた研究成果の公表は，研究過程において本研究のさらなる展開が見込まれたので最終年度前年度申請をした結果，H30年度から新たに採択された基盤研究（C）において継続して取り組む。This research aimed to investigate the cultural policies of Austrofascism (1934-1938) and their influence on authors and literature programs broadcasted by radio in Vienna by analyzing radio magazines and newspapers of the 1930s.The project’s first stage focused on exploring relevant research documents concerned with the ideology of Austrofascism. In the second stage, an analysis was conducted of the literary works featured in radio programs of the 1930s in Vienna, which centered on cultural context and the writings of Viennese authors, especially George Saiko (1892-1962), associated with Austrofascism.The project’s principle investigator is continuing to conduct research and publish articles focusing on the cultural policies of Austrofascism in a current on-going project funded by a 2018-2022 Grant-in-Aid for Scientific Research (C).研究課題/領域番号:15K16711, 研究期間(年度):2015-04-01 – 2019-03-31出典：研究課題「ラジオ放送の文学番組からみるオーストリア・ファシズムの文化政策とその影響」課題番号15K16711（KAKEN：科学研究費助成事業データベース（国立情報学研究所）） （https://kaken.nii.ac.jp/ja/report/KAKENHI-PROJECT-15K16711/15K16711seika/)を加工して作

Deep Learning for Information Triage on Twitter

In this paper, we present a Deep Learning-based system for the support of information triaging on Twitter during emergency situations, such as disasters, or other influential events, such as political elections. The system is based on the assumption that a different type of information is required right after the event and some time after the event occurs. In a preliminary study, we analyze the language behavior of Twitter users during two kinds of influential events, namely, natural disasters and political elections. In the study, we analyze the credibility of information included by users in tweets in the above-mentioned situations, by classifying the information into two kinds: Primary Information (first-hand reports) and Secondary Information (second-hand reports, retweets, etc.). We also perform sentiment analysis of the data to check user attitudes toward the occurring events. Next, we present the structure of the system and compare a number of classifiers, including the proposed one based on Convolutional Neural Networks. Finally, we validate the system by performing an in-depth analysis of information obtained after a number of additional events, including an eruption of a Japanese volcano Ontake on 27 September 2014, as well as heavy rains and typhoons that occurred in 2020. We confirm that the methods works sufficiently well even when trained on data from nearly 10 years ago, which strongly suggests that the model is well-generalized and sufficiently grasps important aspects of each type of classified information