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CSE222- Homework 6 HasMap Solved
In this homework, you will implement two different HashMap classes to perform basic Natural Language Processing operations. Specifically, you will read a text dataset consisting of multiple input files and keep the words in the Word HashMap. The key for the word hashmap will be the words and the value will refer to another hashmap (file hashmap) which keeps the occurrences of the word in different files. The key for the file hashmap is the filename and the value is an arraylist containing the word positions in that file. In order to be able to traverse the word map in an efficient way, each entry should keep a pointer to the next inserted entry, allowing the structure to be Iterable (implements Iterable interface). By this way, the methods such as containsValue(), containsKey() and keySet() can be implemented in a more efficient way, without the need for visiting empty cells in the table. In Figure 1 a sample snapshot of the hashmaps is presented.
After obtaining this structure, you will implement two basic operations used in NLP : retrieving bi-grams and calculating TFIDF values, which are explained below, respectively.
Bi-grams: A bi-gram is simply a piece of text consisting of two sequential words which occurs in a given text at least once. Bi-grams are very informative tools to reveal the semantic relations between words. Let us find the bi-grams in the following text:
“For several years Uganda had been unable to meet its ICO export quota as rebel activity disrupted the coffee industry”
Bi-grams:
For several several years years Uganda Uganda had
had been
...
the coffee coffee industry In Figure 1, the dataset contains a bi-gram “capital city” since capital occurs in the Textfile2 at position 34 and city occurs in the same file at position 35.
TFIDF(Term frequency-inverse document frequency) : This is a score which reflects the importance of a word for a single document. In NLP, a word is informative for a file to be categorized if it occurs frequently in that file but has very few occurrence in other documents in the dataset. To calculate TFIDF, we first calculate the term frequency:
TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).
Then the inverse document frequency is calculated to weigh down the words that are frequent also in other files while scale up the rare ones.
IDF(t) = log(Total number of documents / Number of documents with term t in it)
TFIDF = TF*IDF
Your project will read an input file which consists of multiple queries and print the result of the standard output. There are two main query types. One for retrieving the bi-grams of a word and the other one is for calculating the TFIDF value of a given word for a given filename. A sample input file is given below:
bigram very tfidf coffee 0001978 bigram world bigram costs bigram is tfidf Brazil 0000178
The output should be:
[very difficult, very attractive, very soon, very promising, very rapid, very aggressive, very vulnerable] 0.004878
[world cocoa, world coffee, world market, world prices, world markets, world price, world made, world grain, world tin, world for, world as, world bank, world share]
[costs of, costs and, costs have, costs Transport]
[is flowering, is a, is now, is going, is likely, is expected, is high, is one, is the, is to, is he, is due, is downward, is also, is sceptical, is how, is favourable, is not, is unlikely, is searching, is estimated, is set, is ending, is getting, is very, is passed, is difficult, is being, is still, is showing, is helping, is it, is often, is why, is time, is keeping, is too, is defining, is sold, is uncertain, is insufficient, is wrong, is unrealistic, is put, is currently, is insisting, is unfair, is are, is committed, is 112, is slightly, is forecast, is projected, is at, is sending, is planned, is more, is keen, is heading, is imperative, is no, is faced, is in, is basically, is an, is apparent, is down, is affecting, is willing, is proposing, is fairly, is some, is meeting, is open, is scheduled, is concerned, is possible, is unchanged, is trimming, is Muda, is improving, is that, is well, is only, is precisely, is great, is beginning, is foreseeable, is harvested, is trying, is caused, is depending, is after, is aimed]
After obtaining this structure, you will implement two basic operations used in NLP : retrieving bi-grams and calculating TFIDF values, which are explained below, respectively.
Bi-grams: A bi-gram is simply a piece of text consisting of two sequential words which occurs in a given text at least once. Bi-grams are very informative tools to reveal the semantic relations between words. Let us find the bi-grams in the following text:
“For several years Uganda had been unable to meet its ICO export quota as rebel activity disrupted the coffee industry”
Bi-grams:
For several several years years Uganda Uganda had
had been
...
the coffee coffee industry In Figure 1, the dataset contains a bi-gram “capital city” since capital occurs in the Textfile2 at position 34 and city occurs in the same file at position 35.
TFIDF(Term frequency-inverse document frequency) : This is a score which reflects the importance of a word for a single document. In NLP, a word is informative for a file to be categorized if it occurs frequently in that file but has very few occurrence in other documents in the dataset. To calculate TFIDF, we first calculate the term frequency:
TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).
Then the inverse document frequency is calculated to weigh down the words that are frequent also in other files while scale up the rare ones.
IDF(t) = log(Total number of documents / Number of documents with term t in it)
TFIDF = TF*IDF
Your project will read an input file which consists of multiple queries and print the result of the standard output. There are two main query types. One for retrieving the bi-grams of a word and the other one is for calculating the TFIDF value of a given word for a given filename. A sample input file is given below:
bigram very tfidf coffee 0001978 bigram world bigram costs bigram is tfidf Brazil 0000178
The output should be:
[very difficult, very attractive, very soon, very promising, very rapid, very aggressive, very vulnerable] 0.004878
[world cocoa, world coffee, world market, world prices, world markets, world price, world made, world grain, world tin, world for, world as, world bank, world share]
[costs of, costs and, costs have, costs Transport]
[is flowering, is a, is now, is going, is likely, is expected, is high, is one, is the, is to, is he, is due, is downward, is also, is sceptical, is how, is favourable, is not, is unlikely, is searching, is estimated, is set, is ending, is getting, is very, is passed, is difficult, is being, is still, is showing, is helping, is it, is often, is why, is time, is keeping, is too, is defining, is sold, is uncertain, is insufficient, is wrong, is unrealistic, is put, is currently, is insisting, is unfair, is are, is committed, is 112, is slightly, is forecast, is projected, is at, is sending, is planned, is more, is keen, is heading, is imperative, is no, is faced, is in, is basically, is an, is apparent, is down, is affecting, is willing, is proposing, is fairly, is some, is meeting, is open, is scheduled, is concerned, is possible, is unchanged, is trimming, is Muda, is improving, is that, is well, is only, is precisely, is great, is beginning, is foreseeable, is harvested, is trying, is caused, is depending, is after, is aimed]
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