Symbolic 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 proofing, in speech recognition, in dialogue based applications, in optical character recognition, in text mining, in spam filtering, in text generation, in automatic summarization, in speech synthesis, in computer assisted learning, in database indexing and Web search, etc. Conversely, it is hard to imagine an "intelligent" machine that would be unable to understand language.
NLP comes in two flavours. Many current approaches to language processing are based on large collections of texts. Statistics and machine learning provide quite good predictions about syntax, meaning and intentions. By contrast, symbolic approaches to NLP give priority to the analysis of structures and to exact computation. Often inspired by cognitive analyses, symbolic NLP takes the word "processing" literally: the ultimate goal is to reproduce computations that human individuals are supposed to perform when talking relevantly.
This course is about symbolic
NLP. 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.
Students are supposed to have followed SD206 (Logic and knowledge representation), or equivalent.
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 read a scientific paper and to comment on it.
- 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 (open questions, no documents).