NAME

Intellexer::API - API client for Intellexer

Perl API client for Intellexer, a webservice that, "enables developers to embed Intellexer semantics products using XML or JSON."

SYNOPSIS

my $api_key = q{...get this from intellexer.com};
my $api = Intellexer::API->new($api_key);
my $response = $api->checkTextSpelling(
    $sample_text,
    'language' => 'ENGLISH',
    'errorTune' => '2',
    'errorBound' => '3',
    'minProbabilityTune' => '2',
    'minProbabilityWeight' => '30',
    'separateLines' => 'true'
);
say $json->encode($response);

DESCRIPTION

An interface to the Intellexer API. This module provides perl methods to all the methods available in the Intellexer API using the same names. This will make it easy for those who want to look for more documentation on the Intellexer API help website

Methods

Topic Modeling

Automatically extract topics from text.

`getTopicsFromUrl($url)`

Accepts a single argument that is a valid URL to a document or webpage.

my $response = $api->getTopicsFromUrl( 'https://perldoc.perl.org/perlsub' );

`getTopicsFromFile($file_path)`

Accepts a single argument that is a valid path to a local text file.

my $response = $api->getTopicsFromFile($file_path);

`getTopicsFromText($text)`

Accepts a single argument that is text to be analyzed.

my $response = $api->getTopicsFromText($text);

Linguistic Processor

Parse an input text stream and extract various linguistic information: detected sentences with their offsets in a source text; text tokens (words of sentences) with their part of speech tags, offsets and lemmas (normal forms); subject-verb-object semantic relations.

Parameters

loadSentences - load source sentences (TRUE by default)
loadTokens - load information about words of sentences (TRUE by default)
loadRelations - load information about extracted semantic relations in sentences (TRUE by default)

`analyzeText($text, %params)`

Accepts the text to be analyzed and optionally one or more of the shown parameters.

my $response = $api->analyzeText(
    $sample_text,
    'loadSentences' => 'True',
    'loadTokens'    => 'True',
    'loadRelations' => 'True'
);

Response Structure

'sentences' - array of processed sentences
    'text' - text of the sentence with offset information
        'content' - the sentence plain text
        'beginOffset' - the start sentence offset in the source text
        'endOffset' - the end sentence offset in the source text
    'tokens' - list of tokens extracted from sentences
        'text' - text of the token with offset information
            'content' - the token plain text
            'beginOffset' - the start token offset in the source sentence
            'endOffset' - the end token offset in the source sentence
        'partOfSpeechTag' - token part of speech tag (the tagset is a generalized version of the Lancaster-Oslo/Bergen (LOB) tagset and consist of 37 tags)
            'lemma' - initial token form
    'relations' - array of extracted subject-verb-object relations
    'tokens' - list of tokens extracted from sentences
        'text' - text of the token with offset information
            'content' - the token plain text
            'beginOffset' - the start token offset in the source sentence
            'endOffset' - the end token offset in the source sentence
        'partOfSpeechTag' - token part of speech tag (the tagset is a generalized version of the Lancaster-Oslo/Bergen (LOB) tagset and consist of 37 tags)
            'lemma' - initial token form
    'relations' - array of extracted subject-verb-object relations
        'subject' - subject field
        'verb' - verb field
        'object' - object field
        'adverbialPhrase' - adverbial phrase field

Sentiment Analyzer

Automatically extracts sentiments (positivity/negativity), opinion objects (e.g., product features with associated sentiment phrases) and emotions (liking, anger, disgust, etc.) in unstructured text data.

You will need the list of currently available ontologies which you can retrieve using the sentimentAnalyzerOntologies method

Parameters

ontology - specify which of the existing ontologies will be used to group the results
loadSentences - load source sentences (FALSE by default)

`sentimentAnalyzerOntologies()`

Accepts no arguments but returns the ontologies available from the API

my $response = $api->sentimentAnalyzerOntologies();

`analyzeSentiments(\@reviews, %params)`

Accepts a reference to a list of reviews and the shown parameters

my @reviews = (
    {
        "id" => "snt1",
        "text" => "YourText"
    },
    {
        "id" => "snt2",
        "text" => "YourText"
    }
);

my $ontology = "Gadgets";
my $response = $api->analyzeSentiments(
    \@reviews,
    'ontology'      => 'Hotels', # required
    'loadSentences' => 'True',   # defaults to false
);

Response Structure

'sentimentsCount' - number of processed reviews
'ontology' - ontology used to group the results
'sentences' - array of processed sentences
    'sid' - review identifier
    'text' - text of the sentence with the sentiment tags (pos - positive words, neg - negative words and obj - sentiment objects)
    'w' - sentiment weight of the sentence (0 - neutral information, <0 - negative information, >0 - positive information)
'opinions' - tree of categorized opinions (opinion objects with sentiment phrases)
    't' - text of the opinion object, opinion phrase or the title of an ontology category
    'w' - sentiment weight of the opinion (negative or positive values are used for opinion phrases, zero values - for objects or ontology categories)
'sentiments' - additional information about the processed reviews
    'author' - author of the review
    'dt' - date and time when the review was written
    'id' - review identifier
    'title' - review title
    'w' - sentiment weight of the review. This parameter is used to classify the whole text of a review as expressing a positive, neutral or negative opinion

Named Entity Recognizer

Identifies elements in text and classifies them into predefined categories such as personal names, names of organizations, position/occupation, nationality, geographical location, date, age, duration and names of events. Additionally allows identifying the relations between named entities.

Parameters

url - The url to parse (when using url based method)
fileName - name of the file to process (when using file processing only
fileSize - size of the file to process in bytes
loadNamedEntities - load named entities (FALSE by default)
loadRelationsTree - load tree of relations (FALSE by default)
loadSentences - load source sentences (FALSE by default)

`recognizeNe(%params)`

Load Named Entities from a document from a given URL. Accepts the shown parameters.

my $response = $api->recognizeNe(
    'url'               => 'https://en.wikipedia.org/wiki/Boogie', # required
    'loadNamedEntities' => 'True',    # load named entities (FALSE by default)
    'loadRelationsTree' => 'True',    # load tree of relations (FALSE by default)
    'loadSentences'     => 'True',    # load source sentences (FALSE by default)
);

Response Structure

'document' - information about the text
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size
'entities' - array of detected entities
    'sentenceIds' - array of sentence identifiers containing extracted entities
    'type' -entity type. Possible values: 0 - Unknown, 1 - Person, 2 - Organization, 3 - Location, 4 - Title, 5 - Position, 6 - Age, 7 - Date, 8 - Duration, 9 - Nationality, 10 - Event, 11 - Url, 12 - MiscellaneousLocation
    'wc' - number of words in the entity
    'text' - entity text
'sentences' - array of processed sentences
'relationsTree' - tree of relations among the detected entities
    'entityText' - entity text
    'sentenceIds' - array of sentence identifiers
    'text' - entity text along with the entity type identifier
    'type' - entity type

`recognizeNeFileContent($file_path, %params)`

Load Named Entities from a file. Accepts a file path and the shown parameters as arguments.

my $response = $api->recognizeNeFileContent(
    $filepath,
    'fileSize'          => $size,
    'loadNamedEntities' => 'True',
    'loadRelationsTree' => 'True',
    'loadSentences'     => 'True',
);

Response Structure

'document' - information about the text
'entities' - array of the detected entities
    'sentenceIds' - array of sentence identifiers
    'type' - entity type. Possible values: 0 - Unknown, 1 - Person, 2 - Organization, 3 - Location, 4 - Title, 5 - Position, 6 - Age, 7 - Date, 8 - Duration, 9 - Nationality, 10 - Event, 11 - Url, 12 - MiscellaneousLocation
    'wc' - number of words in the entity
    'text' - entity text
'sentences' - array of processed sentences
'relationsTree' - tree of relations among the detected entities
    'entityText' -entity text
    'sentenceIds' - array of sentence identifiers containing detected entities
    'text' - entity text along with the entity type identifier
    'type' - entity type

`recognizeNeText($text, %params)`

Load Named Entities from a text. Accepts a sample text and the shown parameters.

my $response = $api->recognizeNeText(
   $sample_text,
   'loadNamedEntities' => 'True',
   'loadRelationsTree' => 'True',
   'loadSentences'     => 'True',
);

Response Structure

'document' - information about the text
'entities' - array of detected named entities
    'sentenceIds' - array of sentence identifiers
    'type' - entity type. Possible values: 0 - Unknown, 1 - Person, 2 - Organization, 3 - Location, 4 - Title, 5 - Position, 6 - Age, 7 - Date, 8 - Duration, 9 - Nationality, 10 - Event, 11 - Url, 12 - MiscellaneousLocation
    'wc' - number of words in the entity
    'text' - entity text
'sentences' - array of processed sentences
'relationsTree' - tree of relations among the detected entities
    'entityText' - entity text
    'sentenceIds' - array of sentence identifiers containing detected entities
    'text' - entity text along with entity type identifier
    'type' - entity type

Summarizer

Automatically generates a summary (short description) of a document with its main ideas. Intellexer Summarizer's unique feature is the possibility to create different kinds of summaries: theme-oriented (e.g., politics, economics, sports, etc.), structure-oriented (e.g., scientific article, patent, news article) and concept-oriented.

Parameters

loadConceptsTree - load a tree of concepts (FALSE by default)
loadNamedEntityTree - load a tree of Named Entities (FALSE by default)
summaryRestriction - determine size of a summary measured in sentences
usePercentRestriction - use percentage of the number of sentences in the original text instead of the exact number of sentences
conceptsRestriction - determine the length of a concept tree
structure - specify structure of the document (News Article, Research Paper, Patent or General)
returnedTopicsCount - determine max count of document topics to return
fullTextTrees - load full text trees
useCache - if TRUE, document content will be loaded from cache if there is any
wrapConcepts - mark concepts found in the summary with HTML bold tags (FALSE by default)

`summarize($url, %params)`

Returns summary data for a document from a given URL. Accepts a valid URL as the first argument then any parameters.

my $response = $api->summarize(
    $url,
   'summaryRestriction'    => '7',
   'returnedTopicsCount'   => '2',
   'loadConceptsTree'      => 'true',
   'loadNamedEntityTree'   => 'true',
   'usePercentRestriction' => 'true',
   'conceptsRestriction'   => '7',
   'structure'             => 'general',
   'fullTextTrees'         => 'true',
   'textStreamLength'      => '1000',
   'useCache'              => 'false',
   'wrapConcepts'          => 'true'
);

Response Structure

'summarizerDoc' - information about the text
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size
'structure' - document structure
'topics' - array of detected document topics
'items' - summary items (important document sentences)
    'text' - text of the summary item
    'rank' - item rank. Larger rank means greater importance of the sentence
    'weight' - item weight
'totalItemsCount' - total number of processed sentences
'conceptTree' - tree of important document concepts
    'sentenceIds' - array of sentence identifiers containing detected concepts
    'text' - concept text
    'w' - concept weight
    'mp' - "main phrase" - meaningful/important concepts used in NE relations tree
    'st' - "status" - concept value change from 0 to 1, if the concept was selected for Rearrange operation
    'children' array - concept tree that consists of root nodes (for ex. retrieval) and children nodes (for ex. information retrieval)
'namedEntityTree' - tree of relations among the detected entities
    'entityText' - entity text
    'sentenceIds' - array of sentence identifiers containing detected entities
    'text' - entity text
    'w' - entity weight

`summarizeText($text, %params)`

Return summary data for a text. Accepts text as first argument then any parameters.

my $response = $api->summarizeText( $sample_text, %params );

Response Summary:

    'summarizerDoc' - information about the text
       'id' - document identifier
       'size' - document size in bytes
       'title' - document title
       'url' - source of the request
       'error' - information about processing errors
       'sizeFormat' - formatted document size
   'structure' - document structure
   'topics' - array of detected document topics
   'items' - summary items (important document sentences)
       'text' - text of the summary item
       'rank' - item rank. Larger rank means greater importance of the sentence
       'weight' - item weight
   'totalItemsCount' - total number of processed sentences
   'conceptTree' - tree of important document concepts
       'sentenceIds' - array of sentence identifiers containing  detected concepts
       'text' - concept text
       'w' - concept weight
       'mp' - "main phrase" - meaningful/important concepts used in NE relations tree
       'st' - "status" - concept value change from 0 to 1, if the concept was selected for Rearrange operation
       'children' array - concept tree that consists of root nodes (for ex. retrieval) and children nodes (for ex. information retrieval)
   'namedEntityTree' - tree of relations among the detected entities
       'entityText' - entity text
       'sentenceIds' - array of sentence identifiers containing detected entities
       'text' - entity text
       'w' - entity weight

`summarizeFileContent($file_path, %params)`

Return summary data for a text file. Accepts a file path as first argument then any parameters.

my $response = $api->summarizeFileContent( $file_path, %params );

Response Structure

'summarizerDoc' - information about the text
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size
'structure' - document structure
'topics' - array of detected document topics
'items' - summary items (important document sentences)
    'text' - text of the summary item
    'rank' - item rank. Larger rank means greater importance of the sentence
    'weight' -item weight
'totalItemsCount' - total number of processed sentences
'conceptTree' - tree of important document concepts
    'sentenceIds' - array of sentence identifiers containing detected concepts
    'text' - concept text
    'w' - concept weight
    'mp' - "main phrase" - meaningful/important concepts used in NE relations tree
    'st' - "status" - concept value change from 0 to 1, if the concept was selected for Rearrange operation
    'children' array - concept tree that consists of root nodes (for ex. retrieval) and children nodes (for ex. information retrieval)
'namedEntityTree' - tree of relations among the detected entities
    'entityText' - entity text
    'sentenceIds' - array of sentence identifiers containing detected entities
    'text' - entity text
    'w' - entity weight

Multi-Document Summarizer

With Related Facts automatically generates a summary (short description) from multiple documents with their main ideas. Also it detects the most important facts between the concepts of the selected documents (this feature is called Related Facts).

Parameters

loadConceptsTree - load a tree of concepts (FALSE by default)
loadNamedEntityTree - load a tree of Named Entities (FALSE by default)
summaryRestriction - determine size of a summary measured in sentences
usePercentRestriction - use percentage of the number of sentences in the original text instead of the exact number of sentences
conceptsRestriction - determine the length of a concept tree
structure - specify structure of the document (News Article, Research Paper, Patent or General)
returnedTopicsCount - set max number of document topics to return
relatedFactsRequest - add a query to extract facts and concepts related to it
maxRelatedFactsConcepts - set max number of related facts/concepts to return
maxRelatedFactsSentences - set max number of sample sentences for each related fact/concept
fullTextTrees - load full text trees

`multiUrlSummary(\@url_list, %params)`

Accepts a reference to a list of valid URLs and then any parameters.

my $response = $api->multiUrlSummary(
    \@url_list,
    'filename'              => 'sample.txt',  #required
    'summaryRestriction'    => '7',
    'returnedTopicsCount'   => '2',
    'loadConceptsTree'      => 'true',
    'loadNamedEntityTree'   => 'true',
    'usePercentRestriction' => 'true',
    'conceptsRestriction'   => '7',
    'structure'             => 'general',
    'fullTextTrees'         => 'true',
    'textStreamLength'      => '1000',
    'useCache'              => 'false',
    'wrapConcepts'          => 'true'
);

Response Structure

'documents' - information about the documents
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size
'topics' - array of detected document topics
'structure' - document structure
'items' - multi-document summary items (important document sentences)
    'text' - item text
    'rank' - item rank. Larger rank means greater importance of the sentence
    'weight' - item weight
'conceptTree' - tree of important document concepts
    'mp' - "main phrase" - meaningful/important concepts used in NE relations tree
    'st' - "status" - concept value change from 0 to 1, if the concept was selected for Rearrange operation
    'children' array - concept tree that consists of root nodes (for ex. retrieval) and children nodes (for ex. information retrieval)
'namedEntityTree' - tree of relations among the detected entities
'relatedFactsQuery' - query for related facts extraction (most important concepts and document sentences related to the query)
'relatedFactsTree' - related facts tree along with the facts about the extracted concepts
    'sentenceIds' - array of sentence containing detected facts
    'text' - fact text
    'w' - fact weight

Comparator

Accurately compares documents of any format and determines the degree of similarity between them.

Parameters

useCache - if TRUE, document content will be loaded from cache if there is any

Response Structure

'proximity' - proximity between documents. The proximity is calculated within the range of 0-1, where 0 means "completely different texts" and 1 means "completely identical texts"
'document1' - information about the first document
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size
'document2' - information about the second document
    'id' - document identifier
    'size' - document size in bytes
    'title' - document title
    'url' - source of the request
    'error' - information about processing errors
    'sizeFormat' - formatted document size

`compareText( $text1, $text2 )`

Compares the specified sources. Accepts two arguments that are text.

my $response = $api->compareText( $sample_text, $sample_text2 );

`compareUrls( $url1, $url2, %params )`

Compares the specified sources. Accepts two arguments that are valid URLs followed by any params.

my $response = $api->compareUrls( $url1, $url2 );

`compareUrlwithFile( $url, $file, %params )`

Compares URL source to a local file. Accepts a valid URL followed by a valid file path followed by any params

my $response = $api->compareUrlwithFile( $url, $filename )

`compareFiles( $file1, $file2 )`

Compares the given sources. Accepts two arguments that are both valid file paths.

my $response = $api->compareFiles('sample.txt','sample2.txt');

Clusterizer

Hierarchically sorts an array of documents or terms from given texts.

Parameters

conceptsRestriction - determine the length of a concept tree
fullTextTrees - load full text trees
useCache - if TRUE, document content will be loaded from cache if there is any ( when using URLs )
loadSentences - load all sentences
wrapConcepts - mark concepts found in the summary with HTML bold tags (FALSE by default)

Response Structure

'conceptTree' - tree of important document concepts
    'sentenceIds' - array of sentence identifiers containing detected concepts
    'text' - concept text
    'w' - concept weight
    'mp' - "main phrase" - meaningful/important concepts used in NE relations tree
    'st' - "status" - concept value change from 0 to 1, if the concept was selected for Rearrange operation
    'children' array - concept tree that consists of root nodes (for ex. retrieval) and children nodes (for ex. information retrieval)
'sentences' - array of processed sentences

`clusterize($url, %params)`

Return tree of concepts for a document from a given URL. Accepts a valid URL followed by any parameters.

my $response = $api->clusterize(
    $url_list[0],
    'conceptsRestriction' => '10',
    'fullTextTrees'       => 'true',
    'loadSentences'       => 'true',
    'wrapConcepts'        => 'true'
);

`clusterizeText($text, %params)`

Return tree of concepts for a text. Accepts text followed by any parameters.

my $response = $api->clusterizeText( $sample_text, %params );

clusterizeFileContent($file, %params)

Return tree of concepts for a text. Accepts a valid file path followed by any parameters.

my $response = $api->clusterizeFileContent($file, %params)

Natural Language Interface

Transforms Natural Language Queries into Boolean queries.

`convertQueryToBool( $text )`

Convert a user query in English to a set of terms and concepts joined by logical operators. Accepts a single argument which is the text to be processed

my $response = $api->convertQueryToBool('I just enter some text here and see what happens');

Preformator

Extracts plain text and information about the text layout from documents of different formats (doc, pdf, rtf, html, etc.).

Parameters

useCache - if TRUE, document content will be loaded from cache if there is any
getTopics - if TRUE, response will contain Topic ID of the document

`supportedDocumentStructures()`

Return available Preformator Document structures.

my $response = $api->supportedDocumentStructures();

`supportedDocumentTopics()`

Return available Preformator Document topics.

my $response = $api->supportedDocumentTopics();

`parse( $url, %params )`

Response Structure

'structure' - document structure
'topics' - array of detected document topics
'lang' - document language
'langId' - language identifier
'inputSize' - size of the document before processing
'size' - size of extracted plain text
'text' - plain text from the input document

Parse internet/intranet file content using Preformator. Accepts a valid URL followed by any parameters.

my $response = $api->parse( $url, 'getTopics' => 'true');

`parseFileContent( $file )`

Parse file content using Preformator. Accepts a single argument that is a valid path to a file.

my $response = $api->parse( $file_path );

Response Structure

'structure' - document structure
'topics' - array of detected document topics
'lang' - document language
'langId' - language identifier
'inputSize' - size of the document before processing
'size' - size of extracted plain text
'text' - plain text from the input document

Language Recognizer

Identifies the language and character encoding of incoming documents.

`recognizeLanguage( $text )`

Recognize language and encoding of an input text stream. Accepts one argument that is the text to be analyzed.

my $response = $api->recognizeLanguage( $text );

Response Structure

'languages' - array of detected languages
'language' - document language
'encoding' - document encoding
'weight' - language weight. Lar

Spellchecker

Automatically corrects spelling errors due to well-chosen statistic and linguistic rules, including: rules for context-dependent misspellings; rules for evaluating the probability of possible corrections; rules for evaluating spelling mistakes caused by different means of representing speech sounds by the letters of alphabet; dictionaries with correct spelling and etc.

Parameters

separateLines - process each line independently
language - set input language
errorTune - adjust 'errorBound' to the length of words according to the expert bound values. There are 3 possible modes:
1. Reduce - choose the smaller value between the expert value and the bound set by the user;
2. Equal - choose the bound set by the user;
3. Raise - choose the bigger value between the expert value and the bound set by the user.
errorBound - manually set maximum number of corrections for a single word regardless of its length
minProbabilityTune - adjust 'minProbabilityWeight' to the length of words according to the expert probability values. Modes are similar to 'errorTune'
minProbabilityWeight - set minimum probability for the words to be included to the list of candidates

`checkTextSpelling($text, %params)`

Perform text spell check. Accepts the text as the first argument, followed by any parameters.

my $result = $api->checkTextSpelling(
    $text,
    'language'             => 'ENGLISH',
    'errorTune'            => 2,
    'errorBound'           => 3,
    'minProbabilityTune'   => 2,
    'minProbabilityWeight' => 30,
    'separateLines'        => 'true'
);

Response Structure

'inputSize' - size of the document
'sentencesCount' - number of processed sentences
'processedSentences' - array of corrected sentences
'sourceSentences' - array of source sentences
'corrections' - array of candidate corrections
    'l' - length of the candidate correction
    'ndx' - index of the sentence with a spelling error
    's' - the offset of the error
    'v' - array of candidate corrections
        't' - correction text
        'w' - correction weight. Larger weight means greater relevance of the candidate correctionmeans greater  relevance of the detected language

ENVIRONMENT

An API key is required to access the Intellexer API. You can get one free for 30 days.

https://www.intellexer.com/

Intellexer.com provides API documentation that this module attempts to follow.

https://esapi.intellexer.com/Home/Help

BUGS

Please report bugs to the Github repository for this project.

https://github.com/haxmeister/Perl-Intellexer-API

AUTHOR

HAX (Joshua S. Day) <haxmeister@hotmail.com>

LICENSE & COPYRIGHT

This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.

To install Intellexer::API, copy and paste the appropriate command in to your terminal.

cpanm

cpanm Intellexer::API

CPAN shell

perl -MCPAN -e shell
install Intellexer::API

For more information on module installation, please visit the detailed CPAN module installation guide.

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`g` `i`	go to github issues (only if github is preferred repository)

	POD
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`g` `c`	Go to changes
`g` `i`	Go to issues
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NAME

SYNOPSIS

DESCRIPTION

Methods

Topic Modeling

getTopicsFromUrl($url)

getTopicsFromFile($file_path)

getTopicsFromText($text)

Linguistic Processor

analyzeText($text, %params)

Sentiment Analyzer

sentimentAnalyzerOntologies()

analyzeSentiments(\@reviews, %params)

Named Entity Recognizer

recognizeNe(%params)

recognizeNeFileContent($file_path, %params)

recognizeNeText($text, %params)

Summarizer

summarize($url, %params)

summarizeText($text, %params)

summarizeFileContent($file_path, %params)

Multi-Document Summarizer

multiUrlSummary(\@url_list, %params)

Comparator

compareText( $text1, $text2 )

compareUrls( $url1, $url2, %params )

compareUrlwithFile( $url, $file, %params )

compareFiles( $file1, $file2 )

Clusterizer

clusterize($url, %params)

clusterizeText($text, %params)

clusterizeFileContent($file, %params)

Natural Language Interface

convertQueryToBool( $text )

Preformator

supportedDocumentStructures()

supportedDocumentTopics()

parse( $url, %params )

parseFileContent( $file )

Language Recognizer

recognizeLanguage( $text )

Spellchecker

checkTextSpelling($text, %params)

ENVIRONMENT

BUGS

AUTHOR

LICENSE & COPYRIGHT

Module Install Instructions

`getTopicsFromUrl($url)`

`getTopicsFromFile($file_path)`

`getTopicsFromText($text)`

`analyzeText($text, %params)`

`sentimentAnalyzerOntologies()`

`analyzeSentiments(\@reviews, %params)`

`recognizeNe(%params)`

`recognizeNeFileContent($file_path, %params)`

`recognizeNeText($text, %params)`

`summarize($url, %params)`

`summarizeText($text, %params)`

`summarizeFileContent($file_path, %params)`

`multiUrlSummary(\@url_list, %params)`

`compareText( $text1, $text2 )`

`compareUrls( $url1, $url2, %params )`

`compareUrlwithFile( $url, $file, %params )`

`compareFiles( $file1, $file2 )`

`clusterize($url, %params)`

`clusterizeText($text, %params)`

`convertQueryToBool( $text )`

`supportedDocumentStructures()`

`supportedDocumentTopics()`

`parse( $url, %params )`

`parseFileContent( $file )`

`recognizeLanguage( $text )`

`checkTextSpelling($text, %params)`