Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
18,219
Table 1. An example of an icon string conversion into seven slots (a), a contruction of a complete sentence (b), and composing a natural language sentence (c).
"... In PAGE 5: ...based approach. Table1 (c) shows the sentence composition from the resulted slots. Table 1.... ..."
Table 1. lexicon encoding both assertions of sentences and order relations between any linguistic expressions. Thus, the formulas of the order calculus that represent assertions of natural language sentences are of the following form. gt; s, s is a derivation tree that derives s. 4.2 Lexicon and ad hoc syntactic rules We use a lexicon that will allow us to demonstrate the main properties of the system in treating monotonic and non-monotonic expressions. The set T0 of primitive types that we use includes the types t (for truth values), e (for entities) and v (for natural numbers). The primitive PO types in T0 po are t and v and the only primitive boolean type is t. The set of primitive categories with their corresponding types (assigned by the type0 function) is: s:t
2002
Cited by 2
Table I: Each sentence S1 entails sentence S2 because their determiners or verbs are in the entailment relation. The UNO natural language processing system correctly computes such entailments.
Table II: Each sentence S1 entails sentence S2 because their noun phrases, verbs, adjectives, adverbs, or prepositions are in the entailment relation. The UNO natural language processing system correctly computes such entailments.
Table 5.2 Next is the test for the text-based search function. In the search text field, the users can input anything that related to their tasks in collecting background information for breaking news, such as several separate words or a natural language sentence. Furthermore, the users also have the options to specify a date range which is from 1994 to 1997 when the test was done. The expected outcome of the action taken for the search is a page showing the entire matched results list with a page separation function at the bottom of each page (see Figure A2.6 in Appendix 2). When the users want to view the complete content of a document, they can navigate to the full document page by clicking on the topic of that document. The expected outcome should be a page showing all the information that a document has in the database. An example is given as seen in Figure A2.7 in Appendix 2. Finally, the users can make a new search by enter a query in the top of each page at any time, and the results list should be the same. This is an additional function to make the search behavior more convenient in any page.
2005
Table 2: Features used only for phrasal rewrites considered in learning models. NUMCAP, NUMSTOP, PCTSTOP: as above. PRIMARY_PARSES: The number of primary parses given by the natural language parser. SECONDARY_PARSES: The number of secondary parses given by the natural language parser. SGM: The statistical goodness of the rewrite; a measure of how grammatical the sentence or phrase is, given by the parser.
Table 2: The Meaning of Modalities and Tenses
"... In PAGE 3: ... Modality and Tense are also linguistic oriented no- tions which clarify the status of the speci c relation- ship, the corresponding Cpl speci cation and natural language sentence. Table2 shows the their meaning. Cpl gives the possibility to express the cardinality of relationships, as Figure 1 shows.... ..."
Table 5. Natural language understanding: a proposed layered learning application.
"... In PAGE 11: ... Nat- ural language understanding can have a clear hierarchical task decomposition. For example, learned word sense disambiguation could facilitate learned sen- tence parsing, which in turn could facilitate semantic encoding of sentences or paragraphs (see Table5 ). While it is currently not possible in general to learn... ..."
Table 5. Natural language understanding: a proposed layered learning application.
"... In PAGE 11: ... Nat- ural language understanding can have a clear hierarchical task decomposition. For example, learned word sense disambiguation could facilitate learned sen- tence parsing, which in turn could facilitate semantic encoding of sentences or paragraphs (see Table5 ). While it is currently not possible in general to learn... ..."
Table 1), for instance, are due to the agglutinative nature of the Basque language. With regard to the speech test, the input consisted of the speech signal recorded by 36 speakers, each one reading out 50 sentences from the test-set in Ta- ble 1. That is, each sentence was read out by at least three speakers. The input speech resulted in approx- imately 3.50 hours of audio signal. Needless to say, the application that we envisage has to be speaker- independent if it is to be realistic.
"... In PAGE 3: ... Table1 : Main features of the METEUS corpus. 4.... ..."
Results 1 - 10
of
18,219
