My long-term goal is to understand intelligence; understanding its underlying principles would give us access to artificial intelligence (AI), and I believe that learning algorithms are essential in this quest. Learning algorithms could give computers the ability to capture operational knowledge (not necessarily in symbolic / verbal form) from examples.

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. Challenges in NLP are numerous and enrol various components from morphological analyzers to semantic parsers.

My research focuses on information retrieval and on Web search engines.

The goal is the improvement of the state of the art and the current practices in this field, through the development of novel information retrieval models, and by exploiting new data sources. These sources, such as user logs, Wikipedia entries and thesauri are put to use to expand, rewrite and otherwise reorganize user queries. My research interests also lie in taking into account the user’s various intentions in different application contexts.

To achieve this, we develop statistical methods to address the specific needs of information retrieval. My research also include multilingual aspects, i.e. successfully finding relevant documents in a language different from that of the query. The methods developed can be applied in various domains: medical information retrieval, e-commerce, opinion analysis on the Web, etc.

Professor and associate member of MILA, the Montreal Institute for Learning Algorithms, Alain Tapp, is a long-time researcher in physics, computer science, and mathematics. From his early studies in these fields, he now brings his expertise to artificial intelligence and deep learning.