Accession Number : ADA219029


Title :   Connectionism and Compositional Semantics


Descriptive Note : Technical rept. 15 Sep 1986-14 Sep 1991


Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT


Personal Author(s) : Touretzky, David S


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a219029.pdf


Report Date : May 1989


Pagination or Media Count : 18


Abstract : Quite a few interesting experiments have been done applying neural networks to natural language tasks. Without detracting from the value of these early investigations, this paper argues that current neural network architectures are too weak to solve anything but toy language problems. Their downfall is the need for 'dynamic inference', in which several pieces of information not previously seen together are dynamically combined to derive the meaning of a novel input. The first half of the paper defines a hierarchy of classes of connectionist models, from categorizers and associative memories to pattern transformers are dynamic inferencers. Some well-known connectionist models that deal with natural language are shown to be either categorizers or pattern transformers. The second half examines in detail a particular natural language problem: prepositional phase attachment. Attaching a PP to an NP changes its meaning, thereby influencing other attachments. So PP attachment requires compositional semantics; and compositionality in non-toy domains requires dynamic inference. Mere pattern transformers cannot learn the PP attachment task without an exponential training set. Connectionist-style computation still has many valuable ideas to offer, so this is not an indictment of connectionisms's potential. Keywords: Knowledge representation; Computer systems; Natural language; Semantics; Artificial intelligence; Connectionist models.


Descriptors :   *ARTIFICIAL INTELLIGENCE , *SEMANTICS , *COMPUTERS , PATTERNS , TRANSFORMERS , ARCHITECTURE , NATURAL LANGUAGE , HIERARCHIES , NEURAL NETS


Subject Categories : Linguistics
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE