Explain TF-IDF (Term Frequency-Inverse Document Frequency) with example

-

Term Weighting (TF-IDF)

The term weighting refers to the process of calculating and assigning the weight of each word as its importance degree. We may need the process for removing words further as well as stop words for further efficiency. The term frequency and the TF-IDF weight are the popular schemes of weighting words, so they will be described formally, showing their mathematical equations. The term weights may be used as attribute values in encoding texts into numerical vectors. 

We may use the term frequency which is the occurrence of each word in the given text as the scheme of weighting words. It is assumed that the stop words which occur most frequently in texts are completely removed in advance. The words are weighted by counting their occurrences in the given text in this scheme. There are two kinds of term frequency: the absolute term frequency as the occurrence of words and the relative term frequency as the ratio of their occurrences to the maximal ones. The relative term frequency is preferred to the absolute one, in order to avoid the overestimation and underestimation by the text lengths.

TF-IDF (Term Frequency-Inverse Document Frequency) is the most popular scheme of weighting words in the area of information retrieval but requires references to the entire text collection which is called corpus. The word weights that are computed by the TF-IDF scheme are proportional to the occurrences in the given text but reversely proportional to that in other texts.



Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more

Suppose that a data warehouse consists of the three dimensions time, doctor, and patient, and the two measures count and charge, where a charge is the fee that a doctor charges a patient for a visit. a) Draw a schema diagram for the above data warehouse using one of the schemas. [star, snowflake, fact constellation] b) Starting with the base cuboid [day, doctor, patient], what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? c) To obtain the same list, write an SQL query assuming the data are stored in a relational database with the schema fee (day, month, year, doctor, hospital, patient, count, charge)

-
Image
Solution: a) Star Schema is shown in the figure below: b) First, we should use the roll-up operation to get the year 2004(rolling-up from the day then month to a year). After getting that, we need to use the slice operation to select (2004). Second, we should use roll-up operation again to get all patients. Then, we need to use the slice operation to select (all). Finally, we get a list of the total fee collected by each doctor in 2004. So, 1. roll up from day to month to year 2. slice for year = “2004” 3. roll up on patient from the individual patient to all 4. slice for patient = “all” 5. get the list of the total fees collected by each doctor in 2004 c) Select doctor, Sum(charge) From fee Where year = 2004 Group by doctor
Read more