The modern time has increased the use of web resources so as it needs the use of more proficient methods and functional search techniques. Those people who are newbie in the field of searching do not know properly how to search. Although, searching in the web is the common goings-on of the researchers. But the new people do not know the clear cut document that they want to search for. They are used to write documents of their interested research areas but they do have incomplete information about their interested areas. They sometimes find difficulties in searching because the present systems for supporting the search are limited. The reason for these limited system are because they are lacking access to the semantic documents and are having difficulties in providing appropriate search patterns. But now some of the advance and intelligent search engines overcoming these limitations by generating and providing more intelligent search engines and also using the user’s primary knowledge. In this regard the term intelligence is used as the capability of the system that deals with the users by natural language dialogue. This procedure helps the engine to know and learn the users’ likes and his profile. Now, the marvelous feedback from the users’ side is helpful in describing the success of this search engine. Now natural dialogue communications can play a vital part in reducing the overloading of the information and having accurate or exact search results. An information retrieval system has been developed that get assistance from the intelligent searching agents.
The agents can operate spider technology by using the web search engines, which have new ways now. These tools are considered as the robots that get training from the users in searching the Web for definite information. Now the agent can be custom-made so that it can make profiles and precise the information of the individual. The intelligent agent can be worked as autonomous as it can be developing judgments about the prone importance of the matters on its own. The intelligence search engine can help the researcher by guiding the web search process; in this regard the most promising method is to discover the priority of the user and his needs either by extracting the in depth knowledge like what the users are looking for or giving the illustrative requests that are intended to focus the users on their topics of interest. Though some searches are based on the Natural Language Processing (NLP) technology that captures the profiles of the users. The Natural Language techniques allows the creation of automatic sentences that permits the system to generate useful dialog among the users and the search engines and also guide them about their preferences. The Natural Language Generating (NLG) system has played a great role in generating effective texts. The effectiveness of these search engines have been seen by Natural Language Feedback. Now those intelligent Web searches that are using NLP technology have been emerged. Their main concern is on question-answer system. In the earlier times, it has focused on targeting the natural language questions in few specific paragraphs of retrieved documents. This only focuses on obtaining the relevant paragraphs rather than generating a dialog and instead of capturing the preferences of the users. The capability of dialog helps in giving the proper feedback to tell the effectiveness of the search engine. After the development of the dialog, the Natural Language Generation for dialog is necessary. This generation is made up of four models. These models are helpful in delimiting the input and output according to the different linguistic and non-linguistic stages information that are extracting from the dialog. These models are, context model: it deals with the participation of the dialog. It is also known as the user model. It concerns the information of the user which he want to search from the web. Interaction model: it is based on two-way or cooperative principles. It involves the two-position structures like question/answer and so on. Dialog analyzer: the third model is consisting of the question from the user’s side and then system analyzes the information that can define the response generation from the system. Dialog generator: the dialog generator collects the information that has been obtained from the search agent and the statement of the dialog and creates the rational statement to the present dialog order.
The next step is to collect the specific question to communicate the system like what is the topic that a researcher wants to search for. And then generations of the general information that are available on the web like what type of information you need for your question. When the user gives a positive response, system gives an opportunity to choose a new search topic. It is not necessary that the search engine is giving all the information that a user is want to collect. Until the agents have to wait for the sufficient knowledge about the goals and feedback of the user. When the interaction will continue to grow it refines and filters the initial information that a user has given as his feedback. The search continues to proceed till it gets the sufficient amount of information for the user like 30 retrieved documents. These researchers have been displayed in vector like criteria that involve the address of the page that have been selected, the language, and the author and so on. When the information that has been extracting from the vectors and the feedback of the user is not enough, the agent is taking some simple decision by performing some actions. These actions are called intelligent search agent actions. To get the feedback the system generates some dialog that are helpful in precising the required information of the user. The context and kind of questions that an agent has made can be changed with the current situations. Different results can be attained for the same interactions. During manipulating and implementing the Natural Language Generation system it is possible to adapt it to personalized situations of the communication.