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Cognitive Approach to Natural Language Processing (SD213)
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Processing language is one of the most important and most challenging issues of Artificial Intelligence. NLP (Natural Language Processing) has many applications. It is commonly used in machine translation, in text mining, in speech recognition, in dialogue based applications, in text generation, in automatic summarization, in Web search, etc. Conversely, it is hard to imagine an "intelligent" machine that would be unable to understand language.
NLP remains a challenging task. Statistical techniques perform well in domains such as machine translation, but they are intrinsically limited to average meanings and cannot take contextual knowledge into account. This course explores some symbolic alternatives to mere statistics.
Some NLP techniques, like grammar and parsing and ontologies, are classic symbolic methods. Some others are inspired by cognitive modelling. They include procedural semantics, aspect processing, dialogue processing. The point is not only to adopt a "reverse engineering" approach to language, but also to adapt engineering techniques to human requirements to improve efficiency and acceptability. This course presents different NLP methods that are inspired by the study of natural language and of the underlying cognitive processes. The techniques and concepts that will be studied have however a broader scope in artificial intelligence and are used to study reasoning, decision making and symbolic machine learning. They include:

• Syntactic processing using context-free grammars. Basic parsing methods.
• Knowledge representation – Meaning representation – Procedural semantics – Aspect.
• Relevance: interest, newsworthiness, argumentative relevance and processing.

It is recommended that students follow SD206 (Logic and knowledge representation), or equivalent, before registering to SD213, but this is not a requirement. The course will alternate lectures and lab work sessions.

• Answers to questions during lab sessions will be recorded. They will be evaluated when students who are close to failing based on other criteria.
• Students will be asked to perform a small technical study by extending some issue addressed during lab sessions. They will be given the opportunity to present their work during a few minutes at the end of the course. They will also write a four-page report.
• Students will answer a small quiz.