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Understanding Meronymy Transitivity and Word Sense Marking in NLP

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This lecture explores meronymy, dissecting its six types: Component-Integral, Member-Collection, Portion-Mass, Stuff-Object, Feature-Activity, and Place-Area. The discussion addresses the non-universal transitivity of meronymy, providing examples such as why "finger is part of body" is valid while "arm is part of orchestra" is not. Additionally, it delves into marking word sequences with senses in a generative model through an argmax computation, explaining the probability parameters involved in this process. This comprehensive examination aids in understanding computational linguistics and semantics.

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Understanding Meronymy Transitivity and Word Sense Marking in NLP

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  1. CS626/449 : Speech, NLP and the Web/Topics in AI Programming(Lecture 8: Quiz-1: Wordnet, Sense etc.) Pushpak BhattacharyyaCSE Dept., IIT Bombay

  2. Question-1 • Six kinds of meronymy are traditionally recognised: • Component-Integral Object: wheel-car • Member-Collection: tree-forest • Portion-Mass: slice-pie • Stuff-Object: steel-railway track • Feature-Activity: speech-felicitation • Place-Area: oasis-desert • In the light of this, advance a theory of why transitivity is not universal for meronymy relations. • That is, it is alright to say • Finger is part of arm, arm is part of body=> finger is part of body • But not, • An arm is part of musician, musician is part of orchestra=> arm is part of orchestra • Give 5 more examples of unacceptable meronymy transitivity, each highlighting different aspects and instances of the application of your theory. • Question-2 • Clearly explain how a sequence of words will be marked with senses in a generative model. That is, formulate the question as an argmax computation, clearly explaining how each probability parameter is computed.

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