How will the Servlet Work??

A servlet is loaded by the servlet container the first time the servlet is requested. The servlet then forwards the user request, processes it, and returns the response to the servlet container, which in turn sends the response back to the user. After that, the servlet stays in memory waiting for other requests—it will not be unloaded from the memory unless the servlet container sees a shortage of memory. Each time the servlet is requested, however, the servlet container compares the timestamp of the loaded servlet with the servlet class file. If the class file timestamp is more recent, the servlet is reloaded into memory. The main steps are illustrated by the flowchart above.

Enterprise Java Beans(EJB) and Java Server Pages(JSP)

EJB is a managed, server-side component architecture for modular construction of enterprise applications.
The EJB specification is one of several Java APIs in the Java Platform, Enterprise Edition. EJB is a server-side model that encapsulates the business logic of an application.

JSP is a Java technology that allows software developers to dynamically generate HTML, XML or other types of documents in response to a Web client request. The technology allows Java code and certain pre-defined actions to be embedded into static content.

Fig 1 and Fig 2 best illustrates the example of EJB and JSP in application architectures. They will be very useful in the creation of the technologies needed to program web applications in Java using Servlets 2.3, JSP 1.2, EJB 2.0 and client-side programming with JavaScript.

New Architecture

This latest architecture best suits our system, and our prototype will be based on it. We had already set up our server, prepared the HTML, jsp and XML codes, completed a test database, now we are concentrating on the EJB and DOM part.

ref:http://wnws.sourceforge.net/

Prototype

On clicking the "Search WordNet" button, the following definitions will be given,

NOM:ene fason conventionel pou saluer ene dimoune.

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Topic discussed on the 17th October 2008
Starting a prototype.

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Creole WordNet Search - 3.0
Word to search for:

Building Wordnets (Ved)

Main strategies for building wordnets
Expand approach:
translate WordNet synsets to another language and take over the structure
easier and more efficient method
compatible structure with WordNet
vocabulary and structure is close to WordNet but also biased
can exploit many resources linked to Wordnet: SUMO, Wordnet domains, selection restriction from BNC, etc...

Merge approach:
create an independent wordnet in another language and align it with WordNet by generating the appropriate translations
more complex and labor intensive
different structure from WordNet
language specific patterns can be maintained, i.e. very precise substitution patterns


http://www.globalwordnet.org/gwa/BuildingWordnets.ppt

ARCHITECTURE OF WORDNET

Topic discussed on the 19th September 2008
Search on WordNet architecture.
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The diagram below gives a brief overview of the WordNet architecture.

Grinder
The grinder acts as a converter; takes a lexical source file written by lexicographers and converts them into a format that is understandable and updatable for WordNet.
The WordNet Database
The word/synset pairs are stored in the wordnet Database. Nouns and verbs are grouped according to semantic fields, adjectives and adverbs are kept in another file separately.
Lexical Source Files
Each lexical file is assigned a file number for use within the database.

All wordnet applications have been modeled or implemented following the Princeton WordNet. For our project, the EuroWordNet architecture can be taken into consideration as well because it is multilingual (one of the applications of wordnet,like disambiguation, can be found in multilingual WN). So both architectures(Princeton and Euro WN) will be mostly similar. The architecture also allows enrichment of monolingual source lexicons through exploitation of the semantic information encoded in corresponding entries.

Applications of WordNet (2)

Topic discussed on the 5th September 2008
Where wordnet is used, give some applications.
Give detail explanations of the applications.
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Detail Applications of WordNet (WN)
Information retrieval
It has been closely related to organisation and representation of knowledge on the internet. The application of Artificial Intelligence has helped for the retrieval of information, a method of incorporating logic and inference focus on WN.
Wordnet has been used as a semantic lexicon, in which queries have been expanded through keyword design, like in full text retrieval in a communication aid. It can be implemented as a linguistic knowledge tool to represent and interpret the meaning of. The user will be provided with efficient and integrated access to information. The development of a natural language interface can be done using wordnet, to optimise the precision of internet search engines by expanding queries.

Conceptual identification/disambiguation
Semantic disambiguation as follows:

i. All the noun-verb pairs in the sentence are selected.

ii. The most likely meaning of the term is chosen. Internet is used with this goal.

iii. Drawing from the most frequently appearing concepts (step ii), all the nouns are selected in the “glossaries” of each verb and its hierarchical subordinates.

iv. Drawing from the most frequently appearing concepts, all the nouns are selected in the “glossaries” of each noun and its hierarchical subordinates.

v. A formula is applied to calculate the concepts common to the nouns in point’s (iii) and (iv).

Disambiguation is very often and varies wordnet application. IWA/H, ARPA and KRSL have been created to avoid ambiguity, later AutoASC, proved very effective for disambiguation in information retrieval. The product has the latest wordnet gloss definition.

Query expansion
An expansion system was proposed in 1994, based on the calculation the tf-idf (term frequency–inverse document frequency) for the query terms and adding to it half the tf-idf for the WN synonyms for these terms. The benefits of applying wordnet to queries, using a Word Sense Disambiguator (WSD) enhance the search process.

Document classification
Semantic traits are extracted by grammatical categorisation of WN nouns, verbs, adjectives and categorisation of the relevance of data. An algorithm was designed by Judith Klavans for automatically determining the genre of a paper on the grounds of the WN verb categories used.