{"id":null,"code":"TKO_8964","name":{"valueFi":"Textual Data Analysis","valueEn":"Textual Data Analysis","valueSv":"Textual Data Analysis"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534308539,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"The student will:\r\n- apply and further expand the understanding of deep neural network models gained in the Deep Learning in Human Language Technology course\r\n- recognize and understand the most important text analysis tasks typically faced in research and data science industry\r\n- understand what methods and datasets apply to these tasks, and their limitations\r\n- be able to gather relevant data and critically assess its quality as well as the quality of the method output\r\n- be able to independently implement basic text analysis tasks using modern neural network models and Python libraries","valueEn":"The student will:\r\n- apply and further expand the understanding of deep neural network models gained in the Deep Learning in Human Language Technology course\r\n- recognize and understand the most important text analysis tasks typically faced in research and data science industry\r\n- understand what methods and datasets apply to these tasks, and their limitations\r\n- be able to gather relevant data and critically assess its quality as well as the quality of the method output\r\n- be able to independently implement basic text analysis tasks using modern neural network models and Python libraries","valueSv":"The student will:\r\n- apply and further expand the understanding of deep neural network models gained in the Deep Learning in Human Language Technology course\r\n- recognize and understand the most important text analysis tasks typically faced in research and data science industry\r\n- understand what methods and datasets apply to these tasks, and their limitations\r\n- be able to gather relevant data and critically assess its quality as well as the quality of the method output\r\n- be able to independently implement basic text analysis tasks using modern neural network models and Python libraries"}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course focuses on practical applications of the methods introduced especially in the “Deep Learning in Human Language Technology” course to various text mining tasks as typically met in research and data science industry. Rather than introducing the inner workings of these methods, their practical applicability to real-world tasks is pursued.\r\n\r\n- Web crawls and other large collections of textual data, their usage and processing\r\n- Data sourcing, annotation, and quality control\r\n- Information retrieval and document search engines, including modern dense vector representations based on deep learning\r\n- Surface and semantic similarity of texts and its application in clustering\r\n- Document classification and text labeling in practical text mining tasks\r\n- Extraction of relations between entities in text","valueEn":"The course focuses on practical applications of the methods introduced especially in the “Deep Learning in Human Language Technology” course to various text mining tasks as typically met in research and data science industry. Rather than introducing the inner workings of these methods, their practical applicability to real-world tasks is pursued.\r\n\r\n- Web crawls and other large collections of textual data, their usage and processing\r\n- Data sourcing, annotation, and quality control\r\n- Information retrieval and document search engines, including modern dense vector representations based on deep learning\r\n- Surface and semantic similarity of texts and its application in clustering\r\n- Document classification and text labeling in practical text mining tasks\r\n- Extraction of relations between entities in text","valueSv":"The course focuses on practical applications of the methods introduced especially in the “Deep Learning in Human Language Technology” course to various text mining tasks as typically met in research and data science industry. Rather than introducing the inner workings of these methods, their practical applicability to real-world tasks is pursued.\r\n\r\n- Web crawls and other large collections of textual data, their usage and processing\r\n- Data sourcing, annotation, and quality control\r\n- Information retrieval and document search engines, including modern dense vector representations based on deep learning\r\n- Surface and semantic similarity of texts and its application in clustering\r\n- Document classification and text labeling in practical text mining tasks\r\n- Extraction of relations between entities in text"}},{"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":"The course is organized in odd years in period III. Upon agreement with the responsible teacher of the course, the course can be followed in even years independently based on lecture and exercise session recordings.","valueEn":"The course is organized in odd years in period III. Upon agreement with the responsible teacher of the course, the course can be followed in even years independently based on lecture and exercise session recordings.","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 language technology, the deep learning methodology as applied in language technology, and intermediate fluency in Python. These can be obtained e.g. through the courses Introduction to Human Language Technology and Deep Learning in Human Language Technology, which are strongly recommended as prerequisites.","valueEn":"The course assumes prior knowledge of language technology, the deep learning methodology as applied in language technology, and intermediate fluency in Python. These can be obtained e.g. through the courses Introduction to Human Language Technology and Deep Learning in Human Language Technology, which are strongly recommended as prerequisites.","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":"The grading measures both understanding of concepts and the ability to apply the techniques and methods in code and practice. The grade is composed of project work and exam grades.","valueEn":"The grading measures both understanding of concepts and the ability to apply the techniques and methods in code and practice. The grade is composed of project work and exam grades.","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":"Filip Ginter, Sampo Pyysalo","valueEn":"Filip Ginter, Sampo Pyysalo","valueSv":"Filip Ginter, Sampo Pyysalo"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}