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Natural language understanding
Published in Janet Finlay, Alan Dix, An Introduction to Artificial Intelligence, 2020
Parsing may be top down, in which case it starts with the symbol for a sentence and tries to map possible rules to the input (or target) sentence, or bottom up, where it starts with the input sentence and works towards the sentence symbol, considering all the possible representations of the input sentence. The choice of which type of parsing to use is similar to that for top-down or bottom-up reasoning; it depends on factors such as the amount of branching each will require and the availability of heuristics for evaluating progress. In practice, a combination is sometimes used. There are a number of parsing methods. These include grammars, transition networks, context-sensitive grammars and augmented transition networks. As we shall see, each has its benefits and drawbacks.
P
Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
Park's transformation a change of variables represented by a linear matrix multiplication used in the analysis of electric machines. See rotor reference frame. parking on a bus, a priority scheme that allows a bus master to gain control of the bus without arbitration. parse tree the tree that is used for parsing strings of a given language. Parseval's theorem a relationship that states that the integral of the square of the magnitude of a periodic function is the sum of the square of the magnitutde of each harmonic component. Rigorously, suppose that two continuous time signals f 1 (t) and f 2 (t) have corresponding Fourier transforms F1 () and F2 (), and that F2 () is the complex conjugate of F2 (). Then Parseval's theorem states that
Natural Language Understanding
Published in Richard E. Neapolitan, Xia Jiang, Artificial Intelligence, 2018
Richard E. Neapolitan, Xia Jiang
Parsing produces a tree that represents the linguistic components of a sentence such as the subject, verb, and object, and their relationships. The next step, namely semantic interpretation, represents the meaning of the sentence from the parse tree. By meaning we mean a statement that can be added to the knowledge base or a query that can be presented to the knowledge base. For example, suppose the domain of discourse is the blocks world in Section 4.2.2. Suppose further that the sentence is “block a is situated on block b Then the semantics of the sentence is on(a,b) and the semantic interpretation of the sentence must derive this logical formula from the sentence.
A reasoning model for geo-referencing named and unnamed spatial entities in natural language place descriptions
Published in Spatial Cognition & Computation, 2022
Madiha Yousaf, Diedrich Wolter
When it comes to extracting spatial information from natural language text, either classic natural language processing tools like parsing or information retrieval techniques can be applied. Parsing is involved with revealing the sentence structure by constructing a so-called parse tree that reflects the grammatical structure. A parser can thus identify how distinct parts of a sentence are related, in particular identify spatial relationships expressed. The feasibility of creating such parsers has been demonstrated in the previous research (for example (Alazzawi, Abdelmoty & Jones, 2012) and (Hobel, 2016)). In the following, we will, however, argue for not using parsing with two arguments.
A Systematic Review of Deep Learning Approaches for Natural Language Processing in Battery Materials Domain
Published in IETE Technical Review, 2022
Geetanjali Singh, Namita Mittal, Satyendra Singh Chouhan
Parsing is a process of breaking a sentence or word into its main components i.e. into a grammatical structure often referred as parse trees. The two main parsing methods are dependency parsing and constituency parsing. Among which dependency parsing is done by building a parse tree over direct relations between tokens and constituency parsing is a parsing method of constructing syntactical structures. In the table, some model's performance is compared on the WSJ-PTB dataset [16–19]. The important applications where parsing is used in NLP are language modeling, sentiment analysis, text summarization, etc.