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     Telecom Paris
Dep. Informatique & Réseaux

J-L. DessallesHome page

July 2020




Cognitive Approach to Natural Language Processing (SD213)

                                other AI courses

Objectives

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 grammars, 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.

Content

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:

Prerequisites

Students are supposed to have followed SD206 (Logic and knowledge representation), or equivalent.

Topics


Lecture 1 Introduction to symbolic NLP Slides: NLP Intro
Slides: NLP & AI
Slides: Symbolic vs. Dynamic
Lecture 2 Introduction to linguistics Slides: Natural Language
Slides: Intro Linguistics
Lecture 3 Syntax & parsing Slides: Parsing
Lecture 4 Procedural semantics Slides: Procedural semantics
Lecture 5 Knowledge representation (Fabian Suchanek) Slides: Introduction
Slides: Knowledge representation
Slides: Existing knowledge bases and their applications
Slides: Some research in the area
Lecture 6 Word embeddings (Chloe Clavel) Slides: Word embedding (Chloé Clavel)
Lecture 7 Contrast and aspect Slides: Contrast
Slides: Aspect
Lecture 8 Relevance and argumentation Simplicity Theory website
Wikipedia page on BDI (belief-desire-intention)
Slides: Argumentation examples
Slides: Argumentation
Slides: CAN (conflict - abduction - negation)
- Soutenances
Lecture 9 Computing emotional intensity and responsibility
Slides
- Quiz & evaluation
-

Lab sessions

Syntax & parsing
27/04/2020 10:15
Procedural Semantics
18/05/2020 08:30    (Attn: 08h30)
Processing aspect 25/05/2020 10:15
Relevance and argumentation
08/06/2020 10:15

Students are asked to complete the exercises of each session within 7 days.

Evaluation


Project

    
   read →
    2020 Students’ micro-studies     
         

Each student will choose a problem related to the above topics and perform a micro-research on that problem. Students will write a 3-page paper (typical structure: problem, relevant studies, claim, evidence, discussion, bibliography (with weblinks)).

The study should be related to symbolic NLP. The easiest way to do this study is to work on a topic closely related to one of the lab work sessions. You are free, however, to work on any other relevant topic. Be careful to keep it feasible: it’s supposed to be a mini-study.
Caveat: if your study involves statistical aspects, only the symbolic part will be considered in the evaluation. Implementation language should be Prolog or Python (ask in case of problem).

Examples: Extend a grammar to analyze more complex sentences (such as the fist sentence of this section); create a grammar for a different language; extend the lab work on procedural semantics to understand more sentences about chess; or to understand sentences about the genealogy of an actual family; extend the lab work on aspect to include more aspectual words (always, ancient, already, still, ...); create a mini-knowledge base on a specific domain (football, Roland-Garros...) and use CAN (last lab work) to propose interactive dialogues; etc.

Indicate the topic of your study     →    HERE
(you may change your mind at will).
You may also See already chosen projects.

Use this page to upload your slides and your report.

Students may work in pairs. In this case, the respective contributions of each student should appear unambiguously. And the expectations are of course doubled.

You will present your work on the "soutenance" day during no more than 5 minutes (audio). Make sure to make it relevant to the audience. A couple of days before presentations, you will be asked to post a few slides that will be displayed while you are talking.

The project itself can be handed in until the last week.
Please post:

Bibliography