Selected issues concerning the use of logical representations in machine learning of natural language are discussed. It is argued that the flexibility and expressivity of logical representations are particularly useful in more complex natural language learning tasks. A number of inductive logic programming (ILP) techniques for natural language are analysed including the CHILL system, abduction and the incorporation of linguistic knowledge, including active learning. Hybrid approaches integrating ILP with manual development environments and probabilistic techniques are advocated.
|Title of host publication||COMPUTATIONAL LOGIC: LOGIC PROGRAMMING AND BEYOND, PT II|
|Place of Publication||BERLIN|
|Number of pages||15|
|Publication status||Published - 2002|