Index Term Weighting

We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

• Index Term Frequency
  

  This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
  

  TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
  

• Inverse Document Frequency
  

  The inverse of the frequency of a term between all the

  Trial and Error + Persistence = Successful Marketing
  A few honest and successful Internet business owners will attest to the fact that they have made countless marketing mistakes. Many of them will admit that they didn't get it exactly right on the first try. However, practically all will tell you that - without the gumption to take calculated risks and the persistence to follow through - they would have never developed a truly successful online marketing campaign.Let me encourage you to take a few calculated risks with your marketing. Extend your comfort zone, so to speak. Step out of the norm and take a leap of faith every now and then.What am I talking about? Mainly, that most small business owners are very, VERY conservative with their marketing programs. They decide to spend a little money on ezine ads, purchase the least priced classified they can find, run it one time, get no response and quit. It doesn't work that way. Not only will that strategy fail almost every time online, it will fail offline, too.One proven principle of marketing is
  This article aims to provide readers with an overview of the very basics of information retrieval. Understanding these principles can help you to optimise your website content for the search engines and also help you to analyse search engine algorithm changes. However, the details in this article are not intended to describe how modern search engines work, as they use many additional factors, including link analysis.

  Information retrieval (IR) is the science of searching for documents / within documents. Information retrieval techniques form some of the most fundamental elements of web search engine technology. This article will discuss information retrieval in the context of search engines.

  Indexes

  It is unrealistic to remotely access documents in real-time when performing a search, as it would be exceptionally slow and unreliable. Therefore a local index is created, which for search engines is done by a crawler (aka spider). Thus, when you perform a search you are not actually searching the web, but are searching a version of the web as seen and stored by the crawler at some point in the past.

  The index would not usually contain the whole document (this may, however, be stored in a separate document cache), but stores a representation of the terms relevant to the document that is quickly and easily searchable. There are various stages to this process (not all systems will include all of these stages):

  1. Document
     

     This is the document in its raw format with all text, structure and formatting.

  2. Structure Analysis
     

     Recognising headings, paragraphs, titles, bold text, lists, ..., etc.

  3. Lexical Analysis
     

     Converting the characters in the document into a list of words. This process may include analysing digits, hyphens, punctuation and the case of letters. Proper Noun Analysis can use the case and format of words/phrases to identify important information such as names, places, dates and organisations.

  4. Stopwords Removal
     

     The removal of words which occur very often and provide no ability to discriminate between documents. For example: "the", "it", "is". However, it can be seen that some search engines leave these words in the index and remove them at the user query level. This allows "+word" queries to be performed.

  5. Stemming
     

     This is a conflation procedure which reduces variations of a word into a single root. For example, both "worked" and "working" may be reduced to "work". The Porter Stemming Algorithm can be used to perform stemming.

  After these processes have been performed we have a list of index terms for this particular document.

  Index Term Weighting

  We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

  • Index Term Frequency
    

    This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
    

    TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
    

  • Inverse Document Frequency
    

    The inverse of the frequency of a term between all the d

    Copywriter As Blogger - Add Blogging To Your Copywriting Income Stream
    If you're a copywriter and blogger, you'll know that creating a blog, and updating it regularly, is just about the easiest way ever to promote yourself and your copywriting services. The search engines love blogs, and you'll find that within 24 hours of adding a post to your blog, the post will be indexed and gathering attention for you.Blogs also work for your clients - a blog is a fantastic way to promote their business at minimal expense. Unlike advertising the blog stays online, and gets business for your clients for years to come.So how do you get blogging gigs?Pro Blogging Under Contract: Get Blogging Gigs From Your Current Copywriting ClientsThe easiest way is to get blogging gigs from your current clients. Most will have heard of "blogging", so it will be an easy sell. Many will ask you to create a blog for them, and others will jump at the chance of a blog as soon as you mention it. I've been blogging since 2001, and like most copywriters, I offer a blog-creatio
    context of search engines.

    Indexes

    It is unrealistic to remotely access documents in real-time when performing a search, as it would be exceptionally slow and unreliable. Therefore a local index is created, which for search engines is done by a crawler (aka spider). Thus, when you perform a search you are not actually searching the web, but are searching a version of the web as seen and stored by the crawler at some point in the past.

    The index would not usually contain the whole document (this may, however, be stored in a separate document cache), but stores a representation of the terms relevant to the document that is quickly and easily searchable. There are various stages to this process (not all systems will include all of these stages):

    1. Document
       

       This is the document in its raw format with all text, structure and formatting.

    2. Structure Analysis
       

       Recognising headings, paragraphs, titles, bold text, lists, ..., etc.

    3. Lexical Analysis
       

       Converting the characters in the document into a list of words. This process may include analysing digits, hyphens, punctuation and the case of letters. Proper Noun Analysis can use the case and format of words/phrases to identify important information such as names, places, dates and organisations.

    4. Stopwords Removal
       

       The removal of words which occur very often and provide no ability to discriminate between documents. For example: "the", "it", "is". However, it can be seen that some search engines leave these words in the index and remove them at the user query level. This allows "+word" queries to be performed.

    5. Stemming
       

       This is a conflation procedure which reduces variations of a word into a single root. For example, both "worked" and "working" may be reduced to "work". The Porter Stemming Algorithm can be used to perform stemming.

    After these processes have been performed we have a list of index terms for this particular document.

    Index Term Weighting

    We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

    • Index Term Frequency
      

      This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
      

      TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
      

    • Inverse Document Frequency
      

      The inverse of the frequency of a term between all the

      Guide to Project Management
      A project is an assignment or task taken up to achieve a specific goal. The development and introduction of new services or of a management information system are instances of a project. A project is different from the continuous or day-to-day processes of a company. It is confined within cost, time, and quality constraints. As a consequence to it a special team of expertise is appointed to manage a project.Project management as the name suggests is all about nurturing or handling a project. This is done with the aid of requisite knowledge about the project, skills and techniques to complete the project within fixed tenure and resources. Project management involves step-by-step procedure along with a prudent approach towards the project.At first the concerned organization prepares an outline of the project. This includes knowing and writing down what the project is all about, the cost involved in the project, the amount of resources needed. A thought is also given to the tentatively earliest possible
      le. There are various stages to this process (not all systems will include all of these stages):

      1. Document
         

         This is the document in its raw format with all text, structure and formatting.

      2. Structure Analysis
         

         Recognising headings, paragraphs, titles, bold text, lists, ..., etc.

      3. Lexical Analysis
         

         Converting the characters in the document into a list of words. This process may include analysing digits, hyphens, punctuation and the case of letters. Proper Noun Analysis can use the case and format of words/phrases to identify important information such as names, places, dates and organisations.

      4. Stopwords Removal
         

         The removal of words which occur very often and provide no ability to discriminate between documents. For example: "the", "it", "is". However, it can be seen that some search engines leave these words in the index and remove them at the user query level. This allows "+word" queries to be performed.

      5. Stemming
         

         This is a conflation procedure which reduces variations of a word into a single root. For example, both "worked" and "working" may be reduced to "work". The Porter Stemming Algorithm can be used to perform stemming.

      After these processes have been performed we have a list of index terms for this particular document.

      Index Term Weighting

      We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

      • Index Term Frequency
        

        This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
        

        TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
        

      • Inverse Document Frequency
        

        The inverse of the frequency of a term between all the

        How To Learn Great Management from Our Kids
        Learning comes from many places. And one of the most wondrous opportunities is right in front of us. At dinner, at play and at bedtime, every evening. It is there on the sports field, on vacation and during homework. Our children have clues we can use in our business and organisation, right away..Listen to ThemKids tell us a lot about themselves, if we are prepared to listen. Often, especially when they are quite young, they ask us unexpected questions, that, if we are prepared to hear what they are saying to us, can lead us to more questioning. We can sharpen our senses to what else is going on by listening to them, to give us clues as to what else might be going on. What Managers Can Learn HereUsing our senses means that we can understand people well. But how much, as managers, we can get from this depends on how we develop our more subtle senses of intuition and more particularly, our 'hearing' skills. Watch Them at PlayHave you e
        organisations.
      • Stopwords Removal
        

        The removal of words which occur very often and provide no ability to discriminate between documents. For example: "the", "it", "is". However, it can be seen that some search engines leave these words in the index and remove them at the user query level. This allows "+word" queries to be performed.

      • Stemming
        

        This is a conflation procedure which reduces variations of a word into a single root. For example, both "worked" and "working" may be reduced to "work". The Porter Stemming Algorithm can be used to perform stemming.

      After these processes have been performed we have a list of index terms for this particular document.

      Index Term Weighting

      We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

      • Index Term Frequency
        

        This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
        

        TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
        

      • Inverse Document Frequency
        

        The inverse of the frequency of a term between all the

        Affiliate Marketing Explained In An Easy Way To Understand
        Affiliate marketing is getting paid to sell other people's products. Easy enough to understand, but a little hard to actually do because most people go about it wrong. Here are the basics explained in a way you can understand.1. Do not use the affiliate website the merchant gives you. When you join an affiliate program you will get the same exact web page as everyone else except it has your i.d. number coded in it so you get credit for the sale. Don't use this exact same website. Design your own referral or testimonial page. This is called landing page or squeeze page. Offer your own insight into the product. If you own it tell all of the good things you like about it.This is called pre-selling and is the number one mistake most affiliates make. They do not do it at all! Here is your big chance to be different and make more sales in the process. Is this easy enough to understand?2. Get a good hosting company. Buy a domain name that is generic enough that you can build different landing page fo
        e have a list of index terms for this particular document.

        Index Term Weighting

        We now need to calculate to what degree a term is relevant to a particular document. The following is an example of a weighting scheme:

        • Index Term Frequency
          

          This is the frequency of a term inside a document. The frequency is usually normalised within the particular document:
          

          TermFrequency(term, document) = (no. occurrences of term in document) / (no. occurrence of term with max occurrences in document)
          

        • Inverse Document Frequency
          

          The inverse of the frequency of a term between all the documents in the set. Terms that appear in many documents are not very useful as they do not allow us to discriminate between documents.
          

          IDF(term) =

          log([no. documents in collection] / [no. documents in collection containing term])
          

        • Weight
          

          This is the actual index term weight for a particular term in a particular document:
          

          Weight(term, document) = TermFrequency(term, document) * IDF(term)

        Other items may be a factor in deciding weight, such as: the terms position in the document, whether it was in the title, whether it was bold, whether it was in a list, ..., etc.

        Reverse Index

        We now have a list of terms (with their weights) for a given document. However, a list of documents that contain a particular word would be much more useful, rather than a list of words for a particular document. This is called a reverse index.

        For example, if we had the following three documents:

        1. This is a file about website search engine optimisation
        2. A website design tutorial file
        3. A file about bespoke software design and development
        Then the index terms for each document may be as follows (weights would be in parentheses):

        1. file(?), website(?), search(?), engine(?), optimisation(?)
        2. website(?), design(?), tutorial(?), file(?)
        3. file(?), bespoke(?), software(?), design(?), development(?)
        However, the reverse index would be:

        file: document1(?), document2(?), docuement3(?)
        

        website:

        document1(?), document2(?)
        

        search:

        document1(?)
        

        engine:

        document1(?)
        

        optimisation:

        document1(?)
        

        design:

        document2(?), document3(?)
        

        tutorial:

        document2(?)
        

        bespoke:

        document3(?)
        

        software:

        document3(?)
        

        development:

        document3(?)

        The reverse index then allows us to easily find the relevant documents for a particular word

        Similarity Matching

        This is the process for computing the relevance of a document to a particular query. It can comprise:

        • Query Term Weighting
          

          Applies weights to each term in a query. For example, terms at the beginning of a query may be weighted more heavily.

        • Similarity Coefficient
          

          Uses the query term weights and document term weights to compute the similarity between a query and a document. The similarity could be calculated using the vector space model a