[PDF] Top 20 A Knowledge-based approach to understanding natural language

... use of planning theory in natural language understanding systems, and the role of speech act theory in constructing dialo.. gue systems.[r] ... See full document

... A KNOWLEDGE ENGINEERING APPROACH TO NATURAL LANGUAGE UNDERSTANDING Stuart C. A computer system is being developed to handle the acquisition, representation, and use of [r] ... See full document

... Functional Constraints in Knowledge Based Natural Language Understanding F u n c t i o n a l C o n s t r a i n t s i n K n o w l e d g e B a s e d N a t u r a l L a n g u a g e U n d e r s t a n d i n[.] ... See full document

... for the modelling process. The resulting criteria will be evaluated in a detailed e~ample taken from our proto- typical implementation. We hope to contribute some promising st[r] ... See full document

... The grammars used in the NLMenu System are context-free semantic grammars written with phrase structure rules.. These rules may contain the standard abbreviatory conventio[r] ... See full document

... I. This knowledge is stored in the form of pattern-concept pairs. A pattern is a phrasal cons~ruc~ oI varyxng degrees of specificity. A concept is a notation that[r] ... See full document

... LINGUISTIC MEANING AND KNOWLEDGE REPRESENTATION IN AUTOMATIC UNDERSTANDING OF NATURAL LANGUAGE LINGUISTIC MEANING AND KNOWLEDGE REPRESENTATION IN AUTOMATIC UNDERSTANDING OF NATURAL LANGUAGE Eva Haji~o[.] ... See full document

... additional knowledge and sophisticated reasoning to find the ...the language of Answer Set Programming as the knowledge repres- entation and reasoning language for the ...additional ... See full document

... could also increase the coverage of our KG subgraphs from Chapter 5: Instead of projecting a document into the KG, a document would be a fusion of the facts extracted from the document together with the KG’s background ... See full document

... “truly understanding” text or human ...denotational approach for meaning repre- sentation of a programming language, an ideal NLU sys- tem should use denotational semantics to composition- ally map ... See full document

... The resources of our system are sufficient for the evaluated phenomena. However some improvements could be done: particularly a better grammar for questions, relatives and complex propositions. The current resources are ... See full document

... for natural language metadata anno- tation using a synthetic ...of natural language metadata, this dataset does not intersect with the ones we have used in our experiments and for ... See full document

... 5.2 NLU System Description The NLU model we used is a Slot-Gated attention- based bidirectional long short-term memory Model (SGM) (Goo et al., 2018). In our setting, the full-attention setup was used, which ... See full document

... more knowledge of SQL functionalities to the translation function, features such as error detection and query authoring at query asking phase are ...patterns. Natural language queries containing ... See full document

... The working memory stores generally only the information connected to the sentence that is currently being processed p l u s some information necessary to understan[r] ... See full document

... While existing solutions are very valuable, many Big Data problems are not solved / not solved efficiently yet. E.g. summarization of large documents, interpreting data, drawing inference or knowledge discovery, ... See full document

... Natural Language understanding over a set of sentences or a document is a challenging ...We approach this problem using semantic extraction and building an ontolo- gy for answering questions ... See full document

... This work shows that computational linguistic meth- ods, based on general knowledge of language, can be used in large, robust spoken language systems, and that special-[r] ... See full document

... decision based on the output of the visual processor; if that output identifies a target as a word, the fact that it might not fit the context will not be able to overrule the results of the ... See full document

... Image tagging is a difficult and highly relevant task for many machine learning applications. Specifically, with the emerging of online photo services, the image tagging has become a pre-requisite to make the images ... See full document