Steven Schoonover

 What is VerbNet?  Levin Classification  In-depth look at VerbNet  Evolution of VerbNet  What is FrameNet?  Applications

 Lexical resource that organizes English verbs into different classes  Describes verbs based on their syntactic and semantic properties  Originally put together in 2000 by Martha Palmer and Karin Kipper (released in 2005)  Based on the Levin Classification of 1993  Maps to things including WordNet, Xtag, and FrameNet

 Summary of the theoretical work done on semantic-verb classification for the decades leading up to it  Primary verb classes are based upon alternation  Also dependent on extended meaning of verbs and morphology  Taxonomy which provides a classification of 3,024 verbs (4,186 senses)

 Puts these verbs into 48 broad and 192 fine- grained classes according to the 79 alternations with NP and PP complements

 “Break verbs” are verbs which bring about a change on the material integrity of an entity  Characterized by its participation or non- participation in alternations and other constructions  8 different examples

 Causative/inchoative alternation: Tony broke the window The window broke  Middle alternation Tony broke the window The window broke easily  Instrument subject alternation Tony broke the window with the hammer The hammer broke the window

 With/against alternation Tony broke the cup against the wall Tony broke the wall with the cup  Conative alternation Tony broke the window Tony broke at the window  Body-Part possessor ascension alternation Tony broke herself on the arm Tony broke her arm

 Unintentional interpretation available Reflexive object: Tony broke himself Body-part object: Tony broke his finger  Resultative phrase Tony broke the piggy bank open Tony broke the glass to pieces

 Hierarchical domain-independent  Higher level of detail then the original classification of the word in things such as WordNet  Includes both syntactic and semantic information for classes of English verbs based of Levin’s classification  Much more detail than is given in the original classification such as what WordNet has

 Verb classes, like the “Break verbs” example, are described by the set of members, argument structure, restrictions, and frames of the semantic and syntactic predicates  This type of structure allows for a varying level of granularity which can be changed based on the type of NLP application being used

 Syntactic frames consist of thematic roles (such as agent, theme and location), the verb, and any other lexical items needed for alternation or construction of the verb  May also be limited by which prepositions are allowed  Semantic restrictions (such as animate, human, and organization) are used to suggest the preference of thematic roles allowed by the classes

 The first version of VerbNet had 4,100 verb senses (over 3,000 lemmas) distributed across 191 first-level classes, and 74 subclasses  These desciptions used 21 thematic roles, 36 selectional restriction preferences, 314 syntactic frames and 64 semantic predicates  Also dependent on a hierarchy of 57 prepositions

 The first version of VerbNet was evaluated through a mapping to roughly 50,000 instances of PropBank (lexicon of propositions and their arguments) corpus instances  The syntactic frames for VerbNet accounted for over 78% of exact matches found in the frames of PropBank

 In 2006 VerbNet was expanded and from Korhoren and Briscoe (2004) there were 57 new novel classes added to the original Levin classification which VN was based  Also added 106 new diathesis alternations  This added to the coverage of PropBank and boosted it from the once 78% all the way up to 90.86%

 Electronic resource based on a theory called frame semantics (getting the same message across from a different perspective)  Lexical database which contains 1,200 semantic frames, 13,000 lexical units (pairing of a word with a meaning) and over 190,000 example sentences  Mainly created by Charles J. Fillmore, the creator of frame semantics and intial project leader in 1997 when the project began

 VerbNet:  Parameterized Action Representation (work done at Penn which bridges the gap between natural language and animation)  FrameNet:  Question answering, paraphrasing, textual entailment and information extraction  VerbNet and FrameNet  Unified Verb Index

 Consists of VerbNet, PropBank,FrameNet and OntoNotes Sense Groupings  Merges links and webpages from 4 different natural language processing systems  8, 537 total verbs represented  6, 340 VerbNet links  273 VerbNet main classes  214 VerbNet subclasses  5, 649 PropBank links  4, 186 FrameNet links  4, 898 total Grouping Links

 VerbNet  Levin Classification  FrameNet  Applications  Unified Verb Index

 lexical-semantics-iii/     menet.pdf  