String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Method and apparatus for processing natural language with a hierarchy of mapping routines

Abstract: A method and apparatus for processing natural language and deducing meaning from a natural language input characterized by the steps of (a) receiving an ordered string of word objects having a natural language meaning, (b) selecting a word window length, and (c) successively moving the word window along the ordered string and analyzing the meaning of a substring of word objects that fall within the word window. The substring is removed from the ordered string if the substring has a recognized meaning, until all substrings of the ordered string that fit within the window have been analyzed. In a step (d), the word window length is reduced and step (c) is repeated until only an unrecognized residual of the ordered string remains. The meaning of the substring is analyzed by mapping the substring against a database using one or more mapping routines. The mapping routines are preferably arranged in a hierarchy, wherein a successive mapping routine is used to analyze the substring when a previous mapping routine in the hierarchy cannot map the substring. A computer-implemented task is determined from the recognized substrings and performed by the computer system. The apparatus of the present invention implements the method on a pen-based computer system, and the ordered string is preferably received from strokes entered by a stylus on a display screen of the pen-based computer or from a microphone receiving speech input.

Probabilistic representation of formal languages

Abstract: The problem of assigning a probability to each string of a language L(G) generated by a grammar G is considered Two methods are considered One method assigns a probability to each production associated with G and the other assigns the probabilities on the basis of particular features of the language Several necessary conditions that must be satisfied by these probability assignment techniques if they are to be consistant are presented The problem of recognizing languages is also considered It is shown that under some conditions it is possible to recognize a non finitestate language with a finite state acceptor if one is willing to accept a small probability of making an error

Access method data compression with system-built generic dictionaries

Abstract: A computer system constructs a compression dictionary for compressing a character string by interrogating an initial substring portion to determine input string characteristics that are used to select one or more dictionary segments from a library of predetermined dictionary segments individually adapted for compressing strings with particular characteristics. The initial substring portion is dynamically determined during the interrogation. A first set of dictionary segments that meet predetermined automatic selection criteria are selected and a second set of candidate dictionary segments that meet second-level selection criteria are identified for a sampling phase. During the sampling phase, the candidate dictionary segments are alternately used to compress the initial substring portion and determine compression performance statistics. The performance of the dictionary segments in the sampling phase determines which candidate dictionary segments will be added to the first selected dictionary segments, within dictionary total size limits. The first selected dictionary segments and the identified segments constitute a system-built compression dictionary that is used to compress the remainder of the input string. In this way, predetermined compression dictionaries are selected for maximum efficiency in accordance with the data actually being compressed and compression can be carried out quickly and efficiently as input data is received.

Architecture Software Using: A Methodology for Language Development

Abstract: I m p l e m e n t a t i o n . The abstract machine is then given an implementation (typ- ically, a library), or possibly many, to account for different operational con- texts. The valuation function can be implemented as an interpreter based on an abstract machine implementation, or as a compiler to abstract machine instructions. P a r t i a l e v a l u a t i o n . While interpreting is more flexible, compiling is more effi- cient. To get the best of both worlds, we use a program transformation tech- nique, namely, partial evaluation, to automatically transform a DSL program into a compiled program, given only an interpreter. Each of the above methodology steps is further detailed in a separate section of this paper. 1.6 A W o r k i n g E x a m p l e To illustrate our approach, an example of DSL is used throughout the paper. We introduce a simple electronic mail processing application as a working example. Conceptually this application enables users to specify automatic treatments of incoming messages depending on their nature and contents: dispatching mes- sages to people or folders, filtering spam, offering a shell escape (e.g., to feed an electronic agenda), replying to messages when absent, etc. This example is inspired by a Unix program called s l o c a l which offers users a way of processing inbound mail. With s l o c a l , user-defined treatments are expressed in the form of rules. Each rule consists of a string to be searched in a message field (e.g., Subjec t , From) and an action to be performed if the string

On the Power of Polynomial Time Bit-Reductions Extended Abstract

Abstract: For a nondeterministic polynomial time %ring machine M and an input string x, the leaf string of M on x is the 0-1-sequence of leaf-values (0 - reject, 1 - accept) of the computation tree of M with input x. The set A is said to be bit-reducible to B if there exists an M as above such that for every input x, x is in A if and only if the leaf string of M on x is an B. A class C is definable via leaf language B, if C is the class of all languages that are bit-reducible to B. We are interested in the question how complex a leaf language must be in order to characterize some given class C. This question leads to the examination of ihe closure of different language classes under bit-reducibility. We settle this question for subclasses of regular languages, context free languages, and a number of lime and space bounded classes. As consequences we get a number of surprising characterizations for PSPACE.