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- Second-order_co-occurrence_pointwise_mutual_information abstract "Second-order co-occurrence pointwise mutual information is a semantic similarity measure using pointwise mutual information to sort lists of important neighbor words of the two target words from a large corpus. PMI-IR used AltaVista's Advanced Search query syntax to calculate probabilities. Note that the ``NEAR" searchoperator of AltaVista is an essential operator in the PMI-IR method.[citation needed] However, it is no longer in use in AltaVista; this means that, from the implementation point of view, it is not possible to use the PMI-IR method in the same form in new systems. In any case, from the algorithmic point of view, the advantage of using SOC-PMI is that it can calculate the similarity between two words that do not co-occur frequently, because they co-occur with the same neighboring words. For example, the British National Corpus (BNC) has been used as a source of frequencies and contexts. The method considers the words that are common in both lists and aggregate their PMI values (from the opposite list) to calculate the relative semantic similarity. We define the pointwise mutual information function for only those words having ,where tells us how many times the type appeared in the entire corpus, tells us how many times word appeared with word in a context window and is total number of tokens in the corpus. Now, for word , we define a set of words, , sorted in descending order by their PMI values with and taken the top-most words having .The set , contains words , , where andA rule of thumb is used to choose the value of . The -PMI summation function of a word is defined with respect to another word. For word with respect to word it is: where which sums all the positive PMI values of words in the set also common to the words in the set . In other words, this function actually aggregates the positive PMI values of all the semantically close words of which are also common in 's list. should have a value greater than 1. So, the -PMI summation function for word with respect to word having and the -PMI summation function for word with respect to word having are and respectively.Finally, the semantic PMI similarity function between the two words, and , is defined as The semantic word similarity is normalized, so that it provides a similarity score between and inclusively. The normalization of semantic similarity algorithm returns a normalized score of similarity between two words. It takes as arguments the two words, and , and a maximum value, , that is returned by the semantic similarity function, Sim. It returns a similarity score between 0 and 1 inclusively. For example, the algorithm returns 0.986 for words cemetery and graveyard with (for SOC-PMI method).".
- Second-order_co-occurrence_pointwise_mutual_information wikiPageExternalLink 1376815.1376819.
- Second-order_co-occurrence_pointwise_mutual_information wikiPageExternalLink LREC_06_242.pdf.
- Second-order_co-occurrence_pointwise_mutual_information wikiPageID "29662439".
- Second-order_co-occurrence_pointwise_mutual_information wikiPageRevisionID "518844127".
- Second-order_co-occurrence_pointwise_mutual_information hasPhotoCollection Second-order_co-occurrence_pointwise_mutual_information.
- Second-order_co-occurrence_pointwise_mutual_information leadMissing "November 2010".
- Second-order_co-occurrence_pointwise_mutual_information onesource "November 2010".
- Second-order_co-occurrence_pointwise_mutual_information refimprove "November 2010".
- Second-order_co-occurrence_pointwise_mutual_information sections "October 2012".
- Second-order_co-occurrence_pointwise_mutual_information subject Category:Computational_linguistics.
- Second-order_co-occurrence_pointwise_mutual_information subject Category:Statistical_distance_measures.
- Second-order_co-occurrence_pointwise_mutual_information type Abstraction100002137.
- Second-order_co-occurrence_pointwise_mutual_information type Act100030358.
- Second-order_co-occurrence_pointwise_mutual_information type Action100037396.
- Second-order_co-occurrence_pointwise_mutual_information type Choice100161243.
- Second-order_co-occurrence_pointwise_mutual_information type Decision100162632.
- Second-order_co-occurrence_pointwise_mutual_information type Event100029378.
- Second-order_co-occurrence_pointwise_mutual_information type Maneuver100168237.
- Second-order_co-occurrence_pointwise_mutual_information type Measure100174412.
- Second-order_co-occurrence_pointwise_mutual_information type Move100165942.
- Second-order_co-occurrence_pointwise_mutual_information type PsychologicalFeature100023100.
- Second-order_co-occurrence_pointwise_mutual_information type StatisticalDistanceMeasures.
- Second-order_co-occurrence_pointwise_mutual_information type YagoPermanentlyLocatedEntity.
- Second-order_co-occurrence_pointwise_mutual_information comment "Second-order co-occurrence pointwise mutual information is a semantic similarity measure using pointwise mutual information to sort lists of important neighbor words of the two target words from a large corpus. PMI-IR used AltaVista's Advanced Search query syntax to calculate probabilities.".
- Second-order_co-occurrence_pointwise_mutual_information label "Second-order co-occurrence pointwise mutual information".
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- Second-order_co-occurrence_pointwise_mutual_information isPrimaryTopicOf Second-order_co-occurrence_pointwise_mutual_information.