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Página 389
For example , if the auxiliary verb interpretation of “ have ” in the previous
example is built , it will be discarded if no participle , such as “ taken , ” ever
appears . The major disadvantage of this approach is that , because it results in
many ...
For example , if the auxiliary verb interpretation of “ have ” in the previous
example is built , it will be discarded if no participle , such as “ taken , ” ever
appears . The major disadvantage of this approach is that , because it results in
many ...
Página 400
For example , to interpret the sentence about Susan ' s diamond correctly , we
must mark one sense of diamond as ... that is built by the parser contains some
semantic information , although further interpretation may also be necessary .
For example , to interpret the sentence about Susan ' s diamond correctly , we
must mark one sense of diamond as ... that is built by the parser contains some
semantic information , although further interpretation may also be necessary .
Página 408
Attribute of the actor of the conceptualization Suppose that the conceptual
processor were attempting to interpret the prepositional phrase in the sentence
John went to the park with the girl . First , the system ' s immediate memory would
be ...
Attribute of the actor of the conceptualization Suppose that the conceptual
processor were attempting to interpret the prepositional phrase in the sentence
John went to the park with the girl . First , the system ' s immediate memory would
be ...
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Contenido
Problems Problem Spaces and Search | 29 |
7 | 61 |
Knowledge Representation Issues | 105 |
Derechos de autor | |
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Términos y frases comunes
active addition agents algorithm allow answer apply approach assertions becomes belief build called Chapter choose clauses combined complete concept consider consistent constraints contains corresponding define dependency described discussed domain elements example fact Figure function given goal heuristic important initial input instance interpretation John kinds knowledge knowledge base labeled language learning logic look match meaning methods move natural necessary node object occur operators output particular path perform position possible predicate present problem procedure produce properties question reasoning relation represent representation result robot rules semantic sentence shown in Figure shows simple single situation slot solution solve space specific statements step stored structure Suppose task techniques things tree true understanding units usually variables weights