WORDNET Approach on word sense techniques - AKILAN VELMURUGAN

What is WORDNET  Machine readable semantic dictionary interlinked by semantic relations  Developed by PRINCETON University  Large lexical database for English language  Language forms a scale free network with small average shortest path having words as nodes and concepts as links source:

Use of wordnet  Easily navigable  Used as online dictionary for English  Freely for public availability  structure to show relations in the form of  - noun, verb, adjective, adverb  - synonymn  - hypernym (Is a kind of …)  - hyponym (… is a kind of)  - troponym (particular ways to …)  - meronym (parts of...)  WORDNET Application WORDNET Application source:

Few representations of WORDNET  Schema representation  Graph Theory  Tree structure  Force graph structure  wordnet explorer  Visual Interface for wordnet Visual Interface for wordnet

Using RDF Schema and OWL ontology Wordnet classes and properties are represented as wn:word and wn:wordsense Source:

Represented using Graph theory can be directed or un-directed graph Source: www. nodebox.net/code/index.php/Graph

Represented using Tree sturucture uses tokens and lexical relations Source: www. docs.huihoo.com/nltk/0.9.5/en/ch02.html

Represented using Force Graph Structure Presentation of words and meanings as graph nodes, and relations as edges between them Source: www. code.google.com/p/synonym/

Represented for WORDNET Explorer For applying visual principles to Lexical semantics Source:

Flow of study  Background study on wordsense  word ontology  Word Sense Disambiguation  Variable lexical notation for a concept  i-level generic notation  i-level specific notation  Semantic relatedness in WSD  Experiment Results  Thesaurus as a complex network  Visual Interface for wordnet Visual Interface for wordnet WORDNET – synsets – word ontology – set algebra – rules for representing lexical notations – semantic relatedness between concepts – concept distribution statistics – Degree of semantic relatedness :: WSD – Word Sense Disambiguation – semcor – Test cases – WSD on a complex network – WSD in English Thesaurus – Future work Source:

Wordnet – common sense ontology  Symbols are words  Concept meanings are synsets  Represented by one or more wods  Words used for representation: synonymns  Synonyms and polysemous word  Synset comprises a list of words and a list of semantic relations between other sysnsets.  Part I – list of words each one with a list of synsets that the word represents  Part II – set of semantic relations between synsets(is-a, part-of, substance-of, member-of)

WSD: variable lexical notations for a concept GGeneric concept notation: D = I ∪ J ∪ K ∴ J = D − (I ∪ K) = (D − I ) ∩ (D − K) = D ∩ (I ∪ K) J = D ∩ ( I ∩ K) since, B = D ∪ E ∪ F D = B − (E ∪ F) =(B − E) ∩ (B − F) = B ∩ (E ∪ F) D =B ∩ (E ∩ F) Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications ¯¯¯¯ ¯ ¯ ¯ ¯

WSD: variable lexical notations for a concept J = D ∩ ( I ∩ K) =( B ∩ (E ∩ F) ) ∩ ( I ∩ K) J = B ∩ ( (E ∩ F) ∩ ( I ∩ K) ) when J = fly, D = fish lure I = spinner k = troll And introducing boolean operators, AND for ∩ OR for ∪ NOT for ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

WSD: variable lexical notations for a concept  (“fly”) becomes : (“fisherman's lure” OR “fish lure”) AND ( (NOT “spinner”) AND (NOT “troll”) ) then B = lure, E = ground bait, F = stool pigeon  (“fly”) becomes : (“bait” OR “decoy” OR “lure”) AND ( ((NOT “ground bait”) AND (NOT “stoolpigeon”) AND((NOT “spinner”)AND(NOT “troll”)) ) Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

Notation for synset  i-level generic notation for a synset If S k is a synset, F i is the synset that is located i links away following the hypernym links from S k then the i-level generic notation for S k is:  Note: F i is the parent node of F i-1, F i-1 is the parent node of F i-2 …  i-level specific notation for a synset J = P ∪ Q ∪ R when, P = T Q = U R = V ∪ W ∴ J = T ∪ U ∪ (V ∪ W) If S is a synset, L i is the set of synsets, C ik that are located i links away following the hyponym links from S, then the i-level specific regular notation for S is:  Note: if C ik is null, then C (i-1)k would be used (C (i-1)k is a leaf node in the case) Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

WSD: Semantic relatedness and word sense disambiguation  Procedure for determining the semantic relatedness of two given wordnet synsets  Conception 1: Concepts that appear more frequently and closer with each others are "more related" to each others than the concepts that appear less frequently and farther are. Conception 1Synset relatedness measurement conceptsSynset lexical notation close or far of appearanceExists in a web page or not co-occurance frequencyNumber of web pages containing synsets Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

WSD: Semantic relatedness and word sense disambiguation Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

WSD: Tested for four random texts i-level generic notation ( 1, 2, 3 ) Size of windows of context: Target words Vs Context words ( 3, 5, 7 ) Source: Proceedings of the 20th International Conference on Advanced Information Networking and Applications

Thesaurus as a complex network As a Directed Graph:  sink composed of the 73,046 terms with kout = 0  source are the 30,260 terms with at least one outgoing link (kout > 0) – Root words  absolute source : without incoming links kin = 0  normal source : (kout > 0 and kin > 0)  bridge source : without outgoing links to root words (kout(source) = 0) 1 – Normal source 2 – Bridge source 3 – Absolute source 4 – sink Source: arXiv:cond-mat/ v1 2003

Thesaurus as a complex network Frequency of outgoing links Frequency of incoming links Source: arXiv:cond-mat/ v1 2003

Thesaurus as a complex network Incoming Vs Outgoing Frequency Frequency distribution  K out – for root words  K in – for all words  - Root words in K out  - All words in K in  - Root words in K in  - Non root words in K in

Extension of wordnet  Transforming a Tree structure to a Matrix structure  Wordnet in other languages (japanese, korean, Thai)  Imagenet interlinked with wordnet  REBUILDER – a repository of software designs  Retrieves using bayesian network and wordnet