nlp

Value Members

1. object $dummy

   

   Hacking scaladoc issue-8124.

   Hacking scaladoc issue-8124. The user should ignore this object.

2. def bigram(p: Double, minFreq: Int, text: String*): Array[nlp.collocation.Bigram]

   

   Identify bigram collocations whose p-value is less than the given threshold.

   Identify bigram collocations whose p-value is less than the given threshold.

   p

   the p-value threshold

   minFreq

   the minimum frequency of collocation.

   text

   input text.

   returns

   significant bigram collocations in descending order of likelihood ratio.

3. def bigram(k: Int, minFreq: Int, text: String*): Array[nlp.collocation.Bigram]

   

   Identify bigram collocations (words that often appear consecutively) within corpora.

   Identify bigram collocations (words that often appear consecutively) within corpora. They may also be used to find other associations between word occurrences.

   Finding collocations requires first calculating the frequencies of words and their appearance in the context of other words. Often the collection of words will then requiring filtering to only retain useful content terms. Each n-gram of words may then be scored according to some association measure, in order to determine the relative likelihood of each n-gram being a collocation.

   k

   finds top k bigram.

   minFreq

   the minimum frequency of collocation.

   text

   input text.

   returns

   significant bigram collocations in descending order of likelihood ratio.

4. def corpus(text: Seq[String]): SimpleCorpus

   

   Creates an in-memory text corpus.

   Creates an in-memory text corpus.

   text

   a set of text.

5. def df(terms: Array[String], corpus: Array[Map[String, Int]]): Array[Int]

   

   Returns the document frequencies, i.e.

   Returns the document frequencies, i.e. the number of documents that contain term.

   terms

   the token list used as features.

   corpus

   the training corpus.

   returns

   the array of document frequencies.

6. val lancaster: LancasterStemmer { def apply(word: String): String }

   

   The Paice/Husk Lancaster stemming algorithm.

   The Paice/Husk Lancaster stemming algorithm. The stemmer is a conflation based iterative stemmer. The stemmer, although remaining efficient and easily implemented, is known to be very strong and aggressive. The stemmer utilizes a single table of rules, each of which may specify the removal or replacement of an ending.

7. def ngram(maxNGramSize: Int, minFreq: Int, text: String*): Array[Array[nlp.collocation.NGram]]

   

   An Apiori-like algorithm to extract n-gram phrases.

   An Apiori-like algorithm to extract n-gram phrases.

   maxNGramSize

   The maximum length of n-gram

   minFreq

   The minimum frequency of n-gram in the sentences.

   text

   input text.

   returns

   An array of sets of n-grams. The i-th entry is the set of i-grams.

8. implicit def pimpString(string: String): PimpedString

   

9. val porter: PorterStemmer { def apply(word: String): String }

   

   Porter's stemming algorithm.

   Porter's stemming algorithm. The stemmer is based on the idea that the suffixes in the English language are mostly made up of a combination of smaller and simpler suffixes. This is a linear step stemmer. Specifically it has five steps applying rules within each step. Within each step, if a suffix rule matched to a word, then the conditions attached to that rule are tested on what would be the resulting stem, if that suffix was removed, in the way defined by the rule. Once a Rule passes its conditions and is accepted the rule fires and the suffix is removed and control moves to the next step. If the rule is not accepted then the next rule in the step is tested, until either a rule from that step fires and control passes to the next step or there are no more rules in that step whence control moves to the next step.

10. def postag(sentence: Array[String]): Array[PennTreebankPOS]

    

    Part-of-speech taggers.

    Part-of-speech taggers.

    sentence

    a sentence that is already segmented to words.

    returns

    the pos tags.

11. def tfidf(bag: Array[Double], n: Int, df: Array[Int]): Array[Double]

    

    Converts a bag of words to a feature vector by TF-IDF, which is normalized to L2 norm 1.

    Converts a bag of words to a feature vector by TF-IDF, which is normalized to L2 norm 1.

    bag

    the bag-of-words feature vector of a document.

    n

    the number of documents in training corpus.

    df

    the number of documents containing the given term in the corpus.

    returns

    TF-IDF feature vector

12. def tfidf(corpus: Array[Array[Double]]): Array[Array[Double]]

    

    Converts a corpus to TF-IDF feature vectors, which are normalized to L2 norm 1.

    Converts a corpus to TF-IDF feature vectors, which are normalized to L2 norm 1.

    corpus

    the corpus of documents in bag-of-words representation.

    returns

    a matrix of which each row is the TF-IDF feature vector.

13. def vectorize(terms: Array[String], bag: Set[String]): Array[Int]

    

    Converts a binary bag of words to a sparse feature vector.

    Converts a binary bag of words to a sparse feature vector.

    terms

    the token list used as features.

    bag

    the bag of words.

    returns

    an integer vector, which elements are the indices of presented feature tokens in ascending order.

14. def vectorize(terms: Array[String], bag: Map[String, Int]): Array[Double]

    

    Converts a bag of words to a feature vector.

    Converts a bag of words to a feature vector.

    terms

    the token list used as features.

    bag

    the bag of words.

    returns

    a vector of frequency of feature tokens in the bag.