{"id":null,"code":"TKO_7095","name":{"valueFi":"Introduction to Human Language Technology","valueEn":"Introduction to Human Language Technology","valueSv":"Introduction to Human Language Technology"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534334771,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"After completing the course, the student will be able to\r\n - Explain several different language technology applications\r\n - Analyze the most important characteristics of human language as a data source\r\n - Select or create a suitable annotated text corpus for the given task\r\n - Discuss the basic feature representations of language data in machine learning applications\r\n - Implement a simple machine learning pipeline for the given language technology application\r\n - Explain the idea behind semantic vector spaces and summarize the main methods used to learn meaningful vector spaces","valueEn":"After completing the course, the student will be able to\r\n - Explain several different language technology applications\r\n - Analyze the most important characteristics of human language as a data source\r\n - Select or create a suitable annotated text corpus for the given task\r\n - Discuss the basic feature representations of language data in machine learning applications\r\n - Implement a simple machine learning pipeline for the given language technology application\r\n - Explain the idea behind semantic vector spaces and summarize the main methods used to learn meaningful vector spaces","valueSv":"After completing the course, the student will be able to\r\n - Explain several different language technology applications\r\n - Analyze the most important characteristics of human language as a data source\r\n - Select or create a suitable annotated text corpus for the given task\r\n - Discuss the basic feature representations of language data in machine learning applications\r\n - Implement a simple machine learning pipeline for the given language technology application\r\n - Explain the idea behind semantic vector spaces and summarize the main methods used to learn meaningful vector spaces"}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course introduces the use of human language as data in data analysis or machine learning, starting from the concepts of textual corpora and corpus annotation, and continuing to building simple language technology applications. The basic feature representation methods of textual data are explained and their practical implementations are shown while building machine learning pipelines for selected language technology applications. The course also introduces the notion of semantic textual similarity and semantic vector spaces of languages. Language modeling is introduced as a task to learn such representations. The course will also introduce several language technology applications, starting from elementary text processing such as segmentation, and continuing to selected end-user applications such as text classification and sentiment mining.","valueEn":"The course introduces the use of human language as data in data analysis or machine learning, starting from the concepts of textual corpora and corpus annotation, and continuing to building simple language technology applications. The basic feature representation methods of textual data are explained and their practical implementations are shown while building machine learning pipelines for selected language technology applications. The course also introduces the notion of semantic textual similarity and semantic vector spaces of languages. Language modeling is introduced as a task to learn such representations. The course will also introduce several language technology applications, starting from elementary text processing such as segmentation, and continuing to selected end-user applications such as text classification and sentiment mining.","valueSv":"The course introduces the use of human language as data in data analysis or machine learning, starting from the concepts of textual corpora and corpus annotation, and continuing to building simple language technology applications. The basic feature representation methods of textual data are explained and their practical implementations are shown while building machine learning pipelines for selected language technology applications. The course also introduces the notion of semantic textual similarity and semantic vector spaces of languages. Language modeling is introduced as a task to learn such representations. The course will also introduce several language technology applications, starting from elementary text processing such as segmentation, and continuing to selected end-user applications such as text classification and sentiment mining."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Hands-on programming exercises, electronic exam, course project carried out individually or in pairs","valueEn":"Hands-on programming exercises, electronic exam, course project carried out individually or in pairs","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lecture once a week, hands-on programming exercise session once a week, independent project work","valueEn":"Lecture once a week, hands-on programming exercise session once a week, independent project work","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Materials in Moodle and on course Github","valueEn":"Materials in Moodle and on course Github","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Replaces the earlier course TKO_8966 Johdatus kieliteknologiaan.","valueEn":"Replaces the earlier course TKO_8966 Johdatus kieliteknologiaan.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"The course assumes prior knowledge of Python programming. Students outside the Department of Computing can acquire the required skills e.g. by completing the courses TKO_7107 Introduction to Programming and DIKI1002 Working with Text in Python. Also, elementary understanding of machine learning concepts is expected.","valueEn":"The course assumes prior knowledge of Python programming. Students outside the Department of Computing can acquire the required skills e.g. by completing the courses TKO_7107 Introduction to Programming and DIKI1002 Working with Text in Python. Also, elementary understanding of machine learning concepts is expected.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietojenkäsittelytieteet","valueEn":"Computer Science","valueSv":"Computer Science"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Jenna Kanerva, Filip Ginter","valueEn":"Jenna Kanerva, Filip Ginter","valueSv":"Jenna Kanerva, Filip Ginter"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}