wordnet

I would like to use the wordnet lemmatizer to lemmatize the words in a > a<-c("He saw a see-saw on a sea shore", "she is feeling cold") > a [1] "He saw a see-saw on a sea shore" "she is feeling cold" I convert a into a corpus and do pre-processing steps (like stopword removal, lemmatization etc) > a <- Corpus(VectorSource(a)) I wanted to do the lemmatization in the below way, > filter <- getTermFilter("ExactMatchFilter", a, TRUE) > terms <- getIndexTerms("NOUN", 1, filter) > sapply(terms, getLemma) but I get this error > filter <- getTermFilter("ExactMatchFilter", a, TRUE) Error in .jnew(paste

Hii.. Can anybody help me to find an algorithm in Java code to find synonyms of a search word based on the context and I want to implement the algorithm with WordNet database. For example, "I am running a Java program". From the context, I want to find the synonyms for the word "running", but the synonyms must be suitable according to a context. Let me illustrate a possible approach: Let your sentence be A B C Let each word have synsets i.e. {A:(a1, a2, a3), B:(b1), C:(c1, c2)} Now form possible synset sets: (a1, b1, c1), (a1, b1, c2), (a2, b1, c1) ... (a3, b1, c2) Define function F(a, b, c)

I am trying to write a program to find similarity between two documents, and since im using only english, I decided to use wordnet, but I cannot find a way to link the wordnet with php, I cannot find any wordnet api from php. I saw in the forum some one said (Spudley) he called wordnet from php (using shell_exec() function), Thesaurus class or API for PHP [edited] I would really like to know a method used or some example code, a tutorial perhaps to start using the wordnet with php. many thanks The PHP extension which is linked to from the WordNet site is very old and out of date -- it claims

How can you determine the semantic similarity between two texts in python using WordNet? The obvious preproccessing would be removing stop words and stemming, but then what? The only way I can think of would be to calculate the WordNet path distance between each word in the two texts. This is standard for unigrams. But these are large (400 word) texts, that are natural language documents, with words that are not in any particular order or structure (other than those imposed by English grammar). So, which words would you compare between texts? How would you do this in python? One thing that you

How to tag text using wordnet by word's category (java as a interfacer ) ? Example Consider the sentences: 1) Computers need keyboard , moniter , CPU to work. 2) Automobile uses gears and clutch . Now my objective is , the example sentences have to be tagged as 1st sentence Computer/electronic keyboard/electronic CPU / electronic 2nd sentence Automobile / mechanical gears / mechanical clutch / mechanical some extra example ... "Clutch and gear is monitored using microchip " -> clutch /mechanical , gear/mechanical , microchip / electronic "software used here to monitor hydrogen levels" ->

The context is : I already have clusters of words (phrases actually) resulting from kmeans applied to internet search queries and using common urls in the results of the search engine as a distance (co-occurrence of urls rather than words if I simplify a lot). I would like to automatically label the clusters using semantics, in other words I'd like to extract the main concept surrounding a group of phrases considered together. For example - sorry for the subject of my example - if I have the following bunch of queries : ['my husband attacked me','he was arrested by the police','the trial is