【WSD】the 1st week：WSD's Motivation

1. WSD’s definition
   1. Word sense disambiguation。
   2. Word sense disambiguation is the ability to computationally determine which sense of a word is activated by its use in a particular context.
2. WSD's motivation
   1. The fast development of IT has lead to the exponential growth of unstructured information。As a result，there is an increasing urge to treat this mass of information by means of automatic methods。
   2. Hower，traditional techniques for text mining and information retrieval show their limits when they are applied to such huge collections of data. In fact, these approaches, mostly based on lexicosyntactic analysis of text, do not go beyond the surface appearance of words and, consequently, fail in identifying relevant information formulated with different wordings and in discarding documents which are not pertinent to the user needs.
   3. WSD can potentially provide a major breakthrough in the treatment of large-scale amounts data，while traditional techniques for text mining and information retrieval show their limits when applied to such huge collections of data。
3. Why WSD is difficult？
   WSD has been described as an AI-complete problem,that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence.Its acknowledged difficulty does not originate from a single cause, but rather from a variety of factors.
   1. First, the task lends itself to different formalizations due to fundamental questions
   2. Second, WSD heavily relies on knowledge.
      • In fact, the skeletal procedure of any WSD system can be summarized as follows: given a set of words (e.g., a sentence or a bag of words), a technique is applied which makes use of one or more sources of knowledge to associate the most appropriate senses with words in context.
4. WSD‘s task
   1. Map:identify a map from words to senses。
      • If we disregard the punctuation, we can view a text T as a sequence of words (w1, w2, . . . , wn), and we can formally describe WSD as the task of assigning the appropriate sense(s) to all or some of the words in T, that is, to identify a mapping A from words to senses, such that A(i) ⊆ SensesD(wi ), where SensesD(wi) is the set of senses encoded in a dictionary D for word wi ,1 and A(i) is that subset of the senses of wi which are appropriate in the context T.The mapping A can assign more than one sense to each word wi ∈ T, although typically only the most appropriate sense is selected, that is, | A(i) |= 1.
   2. Classification
      • word senses are the classes, and an automatic classification method is used to assign each occurrence of a word to one or more classes based on the evidence from the context and from external knowledge sources