Sylvain Paillard

In this video, I try to explain the concept of Auto-tagging, which can be considered as the task of extracting terms automatically out of unstructured textual data

Terminology mining, term extraction, term recognition, or glossary extraction, is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus.

In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is essential to the language industry.

From Wikipedia - Terminology extraction

In this log, I speak about Auto-tagging:

• Why we need it
• What is the challenge
• The different approaches: naive term frequency versus global semantic web-service
• The key role of compounds (i.e.: double-worded tags)

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This is my first video log. I will try to address topics more openly and frequently this way.

In this log, I speak about Search and Tag Clouds:

• Difference between Search and Tag Clouds
• Similarity between Search Clouds and Search Autocomplete Suggestions
• Potential to extend their usage for navigation
• Challenge to mix search and tags Clouds together (usage versus document frequency)

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In echo to the ever-increasing popularity of the tag-clouds, the emerging domain of auto-tagging aims to be the solution to populate these attractive visual components without requiring to tag each page individually. While many approaches try to solve this challenge, most of them do not address the real underlying technical challenges. But in order to evaluate how tag-clouds can deliver their full potential, we have to analyze what is at stake for the end-users and understand why tag-cloud could make a real difference in the way to access information.

In this new domain where art can meet with technology, visualization with data mining and repetitive manual efforts with automation, I felt it was interesting to inspect the different components and their roles; and to figure out what is new and what is old, what is solved and what is not, what is possible and what is pure fantasy…

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One of the key challenges in providing a positive customer experience online is to avoid the so-called “dead-ends”, defined as “the unsuccessful attempts to find a product or information” by Jacques Nantel, the well-known Professor of the Department of Marketing of HEC Montréal:

When shopping for a product or searching for information on a website, it may take several attempts for consumers to find what they are looking for. It is suggested that these unsuccessful attempts to find a product or information, i.e., dead-ends, influence consumers’ perception and evaluation of the website usability. Results of this study, conducted with 204 consumers over two different sites, suggest that there is a negative relationship between the number of dead-ends experienced by consumers while shopping online and the perception of the website usability.  In addition, contrary to popular belief, a positive relationship was found between the number of pages visited by consumers and their evaluation of the website usability. [...]

The influence of Dead-Ends on perceived website usability

The approach described in the above reference relies on supervised test with over 200 users and is a statistical approach to identify and quantify the dead-ends in a collection of selected web-sites.

I will discuss here some simple methods which can be used to illustrate some Search & Navigation Dead-Ends without any guarantee that they correspond to frequent dead-ends, nor that they represent the majority of the cases, but which do not require any statistical data to identify. The points here-under should therefore be only considered as a checklist to exemplify typical search & navigation dead-ends to open the discussion with a new party on a concrete basis. Advanced statistical tests - like “Clickstream” described in the research above - are highly recommended to validate the significance of the cases of these methods.

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I have spoken (again) as Keynote Speaker at the Webcom event in Montréal the 22nd of October 2009.

The main goal of this speech was to present a case study explaining how Corbeil Electroménagers has implemented a new CEM approach and technology on their E-Commerce web site (here are the slides I presented).

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