How to Do Things With Words

Sustainable Development Goals

中國科技大學通識教育中心英語文證照奬勵金實施要點 中華民國 105 年 1 月 8 日通識教育委員會議通過 一、 中國科技大學(以下簡稱本校)為鼓勵本校學生通過具公信力機構之英語文能力測驗或 取得證照,特訂定「中國科技大學通識教育中心英語文證照獎勵金實施要點」(以下簡 稱本要點)。 二、 學生於就讀本校期間,通過歐盟共同架構(CEFR)語言能力參考指標 B1(中級)同等級英 語文能力測驗以上(含)者,得依據本要點酌予獎勵。檢測項目請參閱本中心「歐洲語言 學習、教學、評量共同參考架構與各英語檢測分級對照表」(參見附表);未列於標準 對照表之測驗項目不給予獎助。 三、 凡本校學生,除應英系外,均得申請。大學部學生通過同等級以申請一次為限,在學期 間得重複申請,但該次申請之級別不得低於前次。 本獎勵金每學期核發乙次,每次核發全校前 10 名,各名次核發金額如附表。 四、 申請人應提供在學期間,申請當(學)期參加考試之證明文件及成績證明或證照,以憑辦 理。 五、 獎勵金申請作業:請至通識教育中心網頁下載「英語文證照獎勵金申請表」(附件 1), 填妥後檢附成績單正本及影本(背面簽名並註明與正本無異)各一份、本人金融帳戶存 簿(郵局或土地銀行)封面影本送至通識教育中心。 通識教育中心得每學期遴選受獎代表,擇期公開頒奬,並辦理後續請款作業。 六、 奬勵金申請期限:通過相關證照考試半年內應提出申請,逾期視同放棄。 七、 本要點之獎勵金由學校開設通識教育中心專戶,一切收支專款專用;每年度如有剩餘 款,則移至翌年度繼續使用。 八、 本要點經通識教育中心會議審查通過,陳請校長核定後公告實施,修訂時亦同。

文化と言語の多様性の中のCommon European Framework of Reference for Languages: Learning, teaching, assessment (CEFR)--それは基準か? (第10回明海大学大学院応用言語学研究科セミナー 講演)

Thank you very much for downloading learning vocabulary in another language. As you may know, people have search numerous times for their favorite novels like this learning vocabulary in another language, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop. learning vocabulary in another language is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the learning vocabulary in another language is universally compatible with any devices to read.

Learning Vocabulary In Another Language

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Question answering (QA) aims at retrieving precise information from a large collection of documents, typically the Web. Different techniques can be used to find relevant information, and to compare these techniques, it is important to evaluate question answering systems. The objective of an Answer Validation task is to estimate the correctness of an answer returned by a QA system for a question, according to the text snippet given to support it. In this article, we present a lexical strategy for deciding if the snippets justify the answers, based on our own question answering system. We discuss our results, and show the possible extensions of our strategy.

Lexical validation of answers in Question Answering

This paper presents the systems we developed while participating in the first task (English Lexical Simplification) of SemEval 2012. Our first system relies on n-grams frequencies computed from the Simple English Wikipedia version, ranking each substitution term by decreasing frequency of use. We experimented with several other systems, based on term frequencies, or taking into account the context in which each substitution term occurs. On the evaluation corpus, we achieved a 0.465 score with the first system.

https://hal.archives-ouvertes.fr/hal-00790866/document

ANNLOR: A Na"ive Notation-system for Lexical Outputs Ranking

This paper studies corpus-based process to select a system-response usable both in chatterbot or as a fallback strategy. It presents, evaluates and compares two selection methods that retrieve and adapt a system-response from the OpenSubtitles2016 corpus given a human-utterance. A corpus of 800 annotated pairs is constituted. Evaluation consists in objective metrics and subjective annotation based on the validity schema proposed in the RE-WOCHAT shared task. Our study indicates that the task of assessing the validity of a system-response given a human-utterance is subjective to an important extent, and is thus a difficult task. Comparisons show that the selection method based on word embedding performs objectively better than the one based on TF-IDF in terms of response variety and response length.

https://hal.archives-ouvertes.fr/hal-01782262/document

Comparing System-response Retrieval Models for Open-domain and Casual Conversational Agent

Sustainable development can be defined as a principle that regulates human activity without causing irreparable damage to the Earth’s natural system. It also aims to preserve resources so that future generations can benefit from them as much as present generations. To address the global challenges today’s world faces to manage the impact of human activity on the environment, the United Nations have defined a set of sustainable development goals to be achieved in the next decade [1]. The climate changes induced by human activities have been accelerating alarmingly as reported by scientists since 1979 [2]. Scientists can observe an increase in pollution (e.g., depletion of oxygen in water, eutrophication), natural resource scarcity, and a significant and accelerated loss of biodiversity. All these changes have led geologists to propose the Anthropocene as a new geological epoch, reflecting the impact of human activities on Earth’s ecosystems[3]. To mitigate this effect, a paradigmatic shift represented by sustainable development is needed in all fields of human activities, including research. As individuals and researchers, we are concerned with these challenges and deeply aware of the necessity to be more sustainable. But in practice, what does this entail? How can a researcher’s activity be “sustainable,” and how do we integrate sustainable practices into research projects? Where do we start? There is currently no global policy from French research institutes to federate collective action of the scientific community towards sustainable development goals, but working groups focusing on sustainable development have published recommendations [4]. There are also examples of good practice in the United Kingdom (S-Labs [5], Laboratory Efficiency Assessment Framework [6]) and the United States (International Institute for Sustainable Laboratories [7], My Green Lab [8]). Taking action to address the emergency situation is not only a moral responsibility we have as citizens but also a necessary contribution to gathering an understanding of the impact of research activities on the environment and how to make them more sustainable. This article is the result of the work carried out by the \"sustainable development\" committee created at the French Laboratoire d’informatique pour la Mécanique et les Sciences de l’Ingénieur (LIMSI) [9] in 2019 to bring together researchers interested in addressing these questions in the context of the activities of our laboratory, which conducts theoretical and experimental research in a diversity of scientific fields, including fluid mechanics, energetics, human language technology, human machine interaction, medical informatics, and augmented and virtual reality. The first task of the committee was to assess the carbon footprint of research activities over the year 2018 [10]. The next task is to analyze the results of this study and draw a roadmap PLOS COMPUTATIONAL BIOLOGY

/pdf/ten-simple-rules-to-make-your-research-more-sustainable-49bgpt1txx.pdf

Ten simple rules to make your research more sustainable.

This study examines two possibilities of using the FLELex graded lexicon for the automated assessment of text complexity in French as a foreign language learning. From the lexical frequency distributions described in FLELex, we derive a single level of difficulty for each word in a parallel corpus of original and simplified texts. We then use this data to automatically address the lexical complexity of texts in two ways. On the one hand, we evaluate the degree of lexical simplification in manually simplified texts with respect to their original version. Our results show a significant simplification effect, both in the case of French narratives simplified for non-native readers and in the case of simplified Wikipedia texts. On the other hand, we define a predictive model which identifies the number of words in a text that are expected to be known at a particular learning level. We assess the accuracy with which these predictions are able to capture actual word knowledge as reported by Dutch-speaking learners of French. Our study shows that although the predictions seem relatively accurate in general (87.4% to 92.3%), they do not yet seem to cover the learners' lack of knowledge very well.

/pdf/evaluating-lexical-simplification-and-vocabulary-knowledge-38aywhq6tn.pdf

Anne-Laure Ligozat

Papers

Lexical validation of answers in Question Answering

ANNLOR: A Na"ive Notation-system for Lexical Outputs Ranking

Comparing System-response Retrieval Models for Open-domain and Casual Conversational Agent

Ten simple rules to make your research more sustainable.

Evaluating Lexical Simplification and Vocabulary Knowledge for Learners of French: Possibilities of Using the FLELex Resource