Towards a Flexible Model of Word Meaning
Magnus Sahlgren · 2002
We would like to build a model of semantic knowledge that have the capacity to acquire and represent semantic informa-tion that is ambiguous, vague and incomplete. Furthermore, the model should be able to acquire this knowledge in an un-supervised fashion from unstructured text data. Such a model needs to be both highly adaptive and very robust. In this sub-mission, we will first try to identify some fundamental prin-ciples that a flexible model of word meaning must adhere to, and then present a possible implementation of these princi-ples in a technique we call Random Indexing. We will also discuss current limitations of the technique and set the direc-tion for future research. Introduction: the nature of meaning Meaning is a key concept in Natural Language Processing