Pengembangan Materi Ajar Bahasa Jerman berbasis Model Pembelajaran NURS dan Google Classroom pada Mahasiswa Program Studi Pendidikan Bahasa Jerman Fakultas Bahasa dan Sastra UNM

1 [email protected] Universitas Negeri Makassar Abstract. The study is an R&D research that aims to obtain data and information related to the development of German language teaching materials based on NURS learning model and Google Classroom. The design of the study used the 4D model, namely define, design, develop, and disseminate. The study was conducted in German Language Education Study Program at FBS UNM with a research sample of 25 second semester students of academic year 2019/2020. The results of the study reveal that the plot of German language teaching material development based on NURS learning model and Google Classroom was pursued through three stages, namely definition, design, and development. Activities at the definition stage include orientation studies and the selection of teaching material formats and instruments (defining of teaching materials form and instrument). The design stage consists of creating (creating) and at the development stage includes improving the quality of teaching materials (reforming of teaching materials) and completion (accomplishment). After the students were given the test, information was obtained that their reading and writing competencies were in good category (gut) with scores of 88.80 and 85.20. The level of validity of the German teaching material was also in very valid category (3.5≤3.8≤4) with the results of data analysis indicated that the student's response to the German language teaching material was very positive (85% ≤87%), the ability of teachers to manage learning was in good category with the implementation level of well-executed category (4≤34.5 <5). In addition, the results of the analysis also show that German language teaching materials based on NURS learning model and Google Classroom are effective in reading learning with the results of tcount (10,975)> ttable (1,714) and writing with tcount (8,292)> ttable (1,714). Keywords: German language teaching materials, NURS learning model, google classroom PENDAHULUAN Bahasa merupakan instrumen yang sangat penting dalam proses komuni

Coping with Data Scarcity: First Steps towards Word Expansion for a Chatbot in the Urban transportation Domain

Hizkuntzaren Prozesamenduan (HP) zenbait arlotan hitzak erabili izan dira tradizionalki zabaltze-tekniken garapenean, hala nola Informazioaren Berreskurapenean (IB) edota Galdera-Erantzun (GE) sistemetan. Master tesi honek bi hurbilpen aurkezten ditu Elkarrizketa-Sistemen (ES) arloan zabaltze-teknikak garatze aldera, zehazkiago Donostiako (Gipuzkoa) hiri-garraiorako chatbot baten ulertze-modulua garatzera zuzendurik. Lehenengo hurbilpenak hitz-bektoreak erabiltzen ditu semantikoki antzekoak diren terminoak erauzteko, kasu honetan FastText-eko aurre-entreinaturiko embedding sorta espainieraz eta bigarren hurbiltzeak hitzen adiera-desanbiguazioa erabiltzen du sinonimoak datu-base lexiko baten bidez erauzteko, kasu honetan espainierazko WordNet-a. Horretarako, ataza kolaboratibo bat diseinatu da, non corpusa osatuko baitugu balizko-egoera erreal baten sarrerak jasoz. Bestalde, domeinuz kanpo dauden sarrerak identi katze aldera, bi esperimentu sorta garatu dira. Lehenengo fasean kali katze sistema bat garatu da, non corpuseko terminoak Term Frequency-Inverse Document Frequency (TF-IDF) erabiliz ordenatzen baitiren eta ondoren kali katze-sistema kosinu-antzekotasunaren bidez osatzen da. Bigarren faseak aurreko kali katze-sistema formalizatuko da, hiru datu-multzo prestatuz eta estrati katuz. Datu-multzo hauek erregresore lineal bat eta Kernel linealarekin euskarri bektoredun makina bat entreinatzeko erabili dira. Emaitzen arabera, aurre-entreinaturiko bektoreek leialtasun handiagoa daukate input errealari dagokionez. Hala ere, datu-base lexikoek estaldura linguistiko zabalagoa gehituko diote zabalduriko corpus hipotetikoari. Azkenik, domeinuaren diskriminazioari dagokionez, emaitzek TF-IDF-tik erauzitako termino gehienen zeukan datu-multzoa hobesten dute.Text expansion techniques have been used in some sub elds of Natural Language Processing (NLP) such as Information Retrieval or Question-Answering Systems. This Master's Thesis presents two approaches for expansion within the context of Dialogue Systems (DS), more precisely for the Natural Language Understanding (NLU) module of a chatbot for the urban transportation domain in San Sebastian (Gipuzkoa). The rst approach uses word vectors to obtain semantically similar terms while the second one involves synonym extraction from a lexical database. For this purpose, a corpus composed of real case scenario inputs has been exploited. Furthermore, the qualitative analysis of the implemented expansion techniques revealed a need to lter out-of-domain inputs. In relation to this problem, two di erent sets of experiments have been carried out. First, the feasibility of using Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity as discrimination features was explored. Then, linear regression and Support Vector Machine (SVM) classi ers were trained and tested. Results show that pre-trained word embedding expansion constitutes a more loyal representation of real case scenario inputs, whereas lexical database expansion adds a wider linguistic coverage to a hypothetically expanded version of the corpus. For out-of-domain detection, increasing the number of features improves both, linear regression and SVM classi cation results

Coping with Data Scarcity: First Steps towards Word Expansion for a Chatbot in the Urban transportation Domain

Hizkuntzaren Prozesamenduan (HP) zenbait arlotan hitzak erabili izan dira tradizionalki zabaltze-tekniken garapenean, hala nola Informazioaren Berreskurapenean (IB) edota Galdera-Erantzun (GE) sistemetan. Master tesi honek bi hurbilpen aurkezten ditu Elkarrizketa-Sistemen (ES) arloan zabaltze-teknikak garatze aldera, zehazkiago Donostiako (Gipuzkoa) hiri-garraiorako chatbot baten ulertze-modulua garatzera zuzendurik. Lehenengo hurbilpenak hitz-bektoreak erabiltzen ditu semantikoki antzekoak diren terminoak erauzteko, kasu honetan FastText-eko aurre-entreinaturiko embedding sorta espainieraz eta bigarren hurbiltzeak hitzen adiera-desanbiguazioa erabiltzen du sinonimoak datu-base lexiko baten bidez erauzteko, kasu honetan espainierazko WordNet-a. Horretarako, ataza kolaboratibo bat diseinatu da, non corpusa osatuko baitugu balizko-egoera erreal baten sarrerak jasoz. Bestalde, domeinuz kanpo dauden sarrerak identi katze aldera, bi esperimentu sorta garatu dira. Lehenengo fasean kali katze sistema bat garatu da, non corpuseko terminoak Term Frequency-Inverse Document Frequency (TF-IDF) erabiliz ordenatzen baitiren eta ondoren kali katze-sistema kosinu-antzekotasunaren bidez osatzen da. Bigarren faseak aurreko kali katze-sistema formalizatuko da, hiru datu-multzo prestatuz eta estrati katuz. Datu-multzo hauek erregresore lineal bat eta Kernel linealarekin euskarri bektoredun makina bat entreinatzeko erabili dira. Emaitzen arabera, aurre-entreinaturiko bektoreek leialtasun handiagoa daukate input errealari dagokionez. Hala ere, datu-base lexikoek estaldura linguistiko zabalagoa gehituko diote zabalduriko corpus hipotetikoari. Azkenik, domeinuaren diskriminazioari dagokionez, emaitzek TF-IDF-tik erauzitako termino gehienen zeukan datu-multzoa hobesten dute.Text expansion techniques have been used in some sub elds of Natural Language Processing (NLP) such as Information Retrieval or Question-Answering Systems. This Master's Thesis presents two approaches for expansion within the context of Dialogue Systems (DS), more precisely for the Natural Language Understanding (NLU) module of a chatbot for the urban transportation domain in San Sebastian (Gipuzkoa). The rst approach uses word vectors to obtain semantically similar terms while the second one involves synonym extraction from a lexical database. For this purpose, a corpus composed of real case scenario inputs has been exploited. Furthermore, the qualitative analysis of the implemented expansion techniques revealed a need to lter out-of-domain inputs. In relation to this problem, two di erent sets of experiments have been carried out. First, the feasibility of using Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity as discrimination features was explored. Then, linear regression and Support Vector Machine (SVM) classi ers were trained and tested. Results show that pre-trained word embedding expansion constitutes a more loyal representation of real case scenario inputs, whereas lexical database expansion adds a wider linguistic coverage to a hypothetically expanded version of the corpus. For out-of-domain detection, increasing the number of features improves both, linear regression and SVM classi cation results

Aplicativo Móvil para reducir los tiempos de espera en los servicios de atención de IAFAS prepagas de Lima utilizando Chatbot con Machine Learning y PLN

Las aseguradoras de salud que están subordinadas a una sola clínica han registrado por primera vez 6,938 reclamos entre el primer semestre del 2021 y el primer semestre del 2022 en la Superintendencia Nacional de Salud (SUSALUD). El 70% de estos reclamos tratan sobre la atención brindada al asegurado que se origina debido al excesivo tiempo de espera al brindar información a los afiliados que ha aumentado tras la congestión de los canales de atención, la falta de plataformas tecnológicas para el afiliado, los insuficientes canales de atención y la demora en la obtención de información de la póliza o atenciones médicas. En consecuencia, el elevado número de reclamos ha tenido un impacto económico paras las IAFAS prepagas ya que ha ocasionado 25 sanciones entre S/. 8,800.00 a S/. 220,000.00. Por ello, se propone el desarrollo de un chatbot que utilice machine learning y procesamiento de lenguaje natural (PLN) para reducir los tiempos de espera en los servicios de atención al asegurado en IAFAS Prepagas de Lima. El modelo propuesto consta de 3 fases: extracción de información mediante técnicas de procesamiento de lenguaje natural, determinación de intención del usuario aplicando el algoritmo de árboles de decisiones y consulta de documentos utilizando servicios Api REST. Esta propuesta fue validada mediante un caso de estudio en una aseguradora prepaga de Lima durante tres días a través de un análisis comparativo de la variable de tiempos de respuesta frente a la variable de uso del aplicativo. Los resultados muestran que el modelo logró reducir los tiempos de espera en 88.43% aproximadamente.Health insurers that are subordinated to a single clinic have registered for the first time 6,938 claims between the first half of 2021 and the first half of 2022 with the National Health Superintendence (SUSALUD). Seventy percent of these claims deal with the attention provided to the insured, which originates due to the excessive waiting time when providing information to the affiliates that has increased after the congestion of the attention channels, the lack of applications for the affiliate, the insufficient attention channels and the delay in obtaining information on the policy or medical attention. Consequently, the high number of claims has had an economic impact for the prepaid IAFAS since it has caused 25 penalties between S/. 8,800.00 and S/. 220,000.00. Therefore, we propose the development of a chatbot that uses machine learning and natural language processing (NLP) to reduce waiting times in policyholder services in prepaid IAFAS in Lima. The proposed model consists of 3 phases: information extraction using natural language processing techniques, determination of user intent by applying the decision tree algorithm and document query using REST Api services. This proposal was validated by means of a case study in a prepaid insurance company in Lima during three days through a comparative analysis of the variable of response times versus the variable of use of the application. The results show that the model to reduce waiting times by approximately 88.43%.Tesi