/**
* Computes similar words and scores from WordNet output based on word type.
*
* @param string $term term to find related thesaurus terms
* @param string $word_type is the type of word such as "NN" (noun),
* "VB" (verb), "AJ" (adjective), or "AV" (adverb)
* (all other types will be ignored)
* @param string $whole_query the original query $term came from
* @return array a sequence of
* (score => array of thesaurus terms) associations. The score
* representing one word sense of term
*/
static function scoredThesaurusMatches($term, $word_type, $whole_query)
{
$word_map = array("VB" => "verb", "NN" => "noun", "AJ" => "adj", "AV" => "adv");
//Gets overview of senses of term[$i] into data
exec(WORDNET_EXEC . " {$term} -over", $data);
if (!$data || !isset($word_map[$word_type])) {
return NULL;
}
$full_name = $word_map[$word_type];
$lexicon_output = implode("\n", $data);
$sense_parts = preg_split("/\\bThe\\s{$full_name}" . '[^\\n]*\\n\\n/', $lexicon_output);
if (!isset($sense_parts[1])) {
return NULL;
}
list($sense, ) = preg_split("/\\bOverview\\sof\\s/", $sense_parts[1]);
$definitions_for_sense = preg_split("/\\d+\\.\\s/", $sense, -1, PREG_SPLIT_NO_EMPTY);
$num_definitions = count($definitions_for_sense);
$sentence = array();
$similar_phrases = array();
$avg_scores = array();
for ($i = 0; $i < $num_definitions; $i++) {
//get sentence fragments examples of using that definition
preg_match_all('/\\"(.*?)\\"/', $definitions_for_sense[$i], $matches);
// to separate out the words
preg_match('/[\\w+\\s\\,\\.\']+\\s\\-+/', $definitions_for_sense[$i], $match_word);
$thesaurus_phrases = preg_split("/\\s*\\,\\s*/", strtolower(rtrim(trim($match_word[0]), "-")));
//remove ori ginal term from thesaurus phrases if present
$m = 0;
foreach ($thesaurus_phrases as $thesaurus_phrase) {
$tphrase = trim($thesaurus_phrase);
if ($tphrase == trim($term)) {
unset($thesaurus_phrases[$m]);
}
$m++;
}
$thesaurus_phrases = array_filter($thesaurus_phrases);
if ($thesaurus_phrases == array()) {
continue;
}
$num_example_sentences = count($matches[1]);
$score = array();
for ($j = 0; $j < $num_example_sentences; $j++) {
$query_parts = explode(' ', strtolower($whole_query));
$example_sentence_parts = explode(' ', strtolower($matches[1][$j]));
$score[$j] = PhraseParser::getCosineRank($query_parts, $example_sentence_parts);
/* If Cosine similarity is zero then go for
* intersection similarity ranking
*/
if ($score[$j] == 0) {
$score[$j] = PhraseParser::getIntersection($query_parts, $example_sentence_parts);
}
}
/* We use the rounded average of the above times 100 as a score
score for a definition. To avoid ties we store in the low
order digits 99 - the definition it was
*/
if ($num_example_sentences > 0) {
$definition_score = 100 * round(100 * (array_sum($score) / $num_example_sentences)) + (99 - $i);
} else {
$definition_score = 99 - $i;
}
$similar_phrases[$definition_score] = $thesaurus_phrases;
}
krsort($similar_phrases);
return $similar_phrases;
}
