NLP applications (II): Building information extraction, question answering and conversational systems
The objective of the subject is to obtain the ability to implement applications based on language technologies/natural language processing. During the course we will learn the basic applications of Natural Language Processing that are currently used in the industry of language technology industry..
The content will focus on the following tasks:
i) Information extraction: We present advanced techniques of lexical disambiguation of multiple various linguistic levels. Disambiguation techniques include word sense disambiguation algorithms, entity linking, and recognition and classification of named entities (NERC). We will learn and implement structured information extraction algorithms, as well as semantic relation and event extraction. For this, the student will be able to use advanced techniques of Deep Learning (embeddings, transfer learning, LSTM, CNN, etc.), sequence labeling (inference, beam search, viterbi, etc.) and distant supervision.
ii) Question Answering: We present unsupervised learning techniques based on semantic textual similarity (embeddings, graph theory), and techniques based on supervised algorithms that include end-to-end methods, information retrieval, and knowledge acquisition . Language generation techniques will also be studied (e.g. language models, seq2seq). The latest advances in multimodal tasks will be studied (e.g. visual question answering)
iii) Conversational systems: We will learn the modules that define a conversational systems, and the algorithms that control the interaction between human and machine. Special emphasis will be given to the natural language comprehension module (NLU) as well as the language generation.
Syllabus
Introduction to NLP applications
Information Extraction and Disambiguation techniques.
