specification tree in a sentence

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1- The model then generates text specification trees .

2- Finally, it generates natural language feature observations conditioned on the hidden specification trees .

3- We denote the probability of choosing a particular specification tree t as P (t).

4- After mutation or crossover operators are applied, a new, possibly invalid specification tree is formed.

5- The No Learning baseline is a variant of our model that selects a specification tree without learning feature correspondence.

6- The specification trees map directly to the WBS which provides traceability between performance requirements and risks for hardware and software items.

7- It continues sampling a specification tree for each text specification until it finds one which successfully reads all of the input examples.

8- A specification tree is rejected in the sampling framework if the corresponding code fails to successfully read all of the input examples.

9- The baseline Full Model (Oracle) is the same as our full model except that the feedback comes from an oracle which tells whether the specification tree is correct or not.

10- Once the algorithm obtains the marginal θ probabilities of all subtrees, a specification tree We use Gibbs sampling to sample variables t from can be drawn recursively in a top-down manner. this distribution.

11- The algorithm works similarly to to estimate the hidden specification trees t given the inside algorithm (Baker, 1979), except that we the observed natural language specifications w, afdo not assume the tree is binary.

12- Note that we do not know this distribution a priori until the specification trees are evaluated by testing the corresponding C++ code.

13- The multinomial parameters provide the probabilities of observing different feature values in the text. • Generating specification tree : For each text specification, draw a specification tree t from all possible trees over the sequence of noun phrases in this specification.

14- The multinomial parameters provide the probabilities of observing different feature values in the text. • Generating specification tree: For each text specification, draw a specification tree t from all possible trees over the sequence of noun phrases in this specification.

15- Our model predicts specification trees t = {t1 , · · · , tN } for the text specifications, where each specification tree ti is a dependency tree over noun phrases {wki }.

16- Our model predicts specification trees t = {t1 , · · · , tN } for the text specifications, where each specification tree ti is a dependency tree over noun phrases {wki }.

17- In this paper, we focus on the NLP techniques used in the first step, i.e., learning to infer the specification trees from text.

18- Second, we convert each specification tree into formal grammar of the input format (represented in Backus-Naur Form) and then generate code that reads the input into data structures.

19- First, given a text specification describing an input format, we wish to infer a parse tree (which we call a specification tree ) implied by the text.

20- The distribution over the space of specification trees is informed by two sources of information: (1) the correlation between the text and the corresponding specification tree and (2) the success of the generated parser in reading input examples.

21- The distribution over the space of specification trees is informed by two sources of information: (1) the correlation between the text and the corresponding specification tree and (2) the success of the generated parser in reading input examples.

22- We focus on the translation from the text specification to the specification tree .2 We assume that each text specification is accompanied by a set of input examples that the desired input parser is required to successfully read.

23- Our method is based on two sources of information: (1) the correlation between the text and the specification tree and (2) noisy supervision as determined by the success of the generated C++ parser in reading input examples.

24- We use a Bayesian generative model to capture relevant natural language phenomena and translate the English specification into a specification tree , which is then translated into a C++ input parser.

25- The sampling framework also rejects the tree if the text/ specification tree pair has low probability.

26- We combine these two kinds of information into a Bayesian generative model in which the code quality of the specification tree is captured by the prior probability P (t) and the feature observations are encoded in the likelihood probability P (w|t).

27- If the matrix does not follow to some degree from the breakdown then the breakdown/matrix should be seriously considered for revision. (Of course, the breakdown must also pass muster against make-or-buy planning, the specification tree , interface working group structure, etc.)

More Sentences:
Related Words:
access treedocumentation treesyntax treetree viewtree-like structuretree-traversalsearch treespecification treespanning tree algorithmcore-based treetree-and-branch roofing treeroof treetree nailcull tree

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