2024年7月23日 · Recursive Transition Networks (RTNs) are a type of finite state machine (FSM) used to represent the syntax of languages, particularly those with recursive elements. They extend FSMs by allowing transitions to call other networks, thereby enabling the representation of recursive structures within a language.

A recursive transition network ("RTN") is a graph theoretical schematic used to represent the rules of a context-free grammar. RTNs have application to programming languages, natural language and lexical analysis. Any sentence that is constructed according to the rules of an RTN [1] is said to be "well-formed". The structural elements of a well ...

Recursive Transition Networks (RTN) RTNs are considered as development for finite state automata with some essential conditions to take the recursive complexion for some definitions in consideration.

To explain what Recursive Transition Networks (RTNs) are, and in particular to explain the role stacks play in controlling their behavior. To show that RTNs are genuinely more powerful than FSAs, and to briefly discuss the link between RTNs and context free grammars.

2024年6月16日 · Augmented Transition Networks (ATNs) are a powerful formalism for parsing natural language, playing a significant role in the early development of natural language processing (NLP). Developed in the late 1960s and early 1970s by William Woods, ATNs extend finite state automata to include additional computational power, making them suitable for ...

5 RTN transducers and ATNs. This lecture has two main goals: To show that we can use RTNs as transducers, and to implement (again by extending our earlier work) a Prolog program that carries out RTN-based transduction. To discuss Augmented Transition Networks (ATNs), a historically important way of extending RTNs that is no longer widely used.

Probabilistic grammars assign a probability to a sentence or string of words. Usually they capture more general syntactic information than the N-gram grammars. Each PCFG rule is treated as if it were conditionally independent; thus the probability of a sentence is computed by multiplying the probabilities of each rule in the parse of the sentence.

Recursive transition networks (RTN) are useful devices for representing grammars in both human and computer languages. The kind of grammars that can be represented with RTN's are called context-free. They consist of rules for how words may be put together into structures and, in turn, how those structures may be built into larger structures.

Recursive Transition Networks (RTNs): RTN is a recursive transition network that permits arc labels to refer to other networks and they, in turn, may refer back to the referring network rather than just permitting word categories used previously.

LLM.int8() 和 SmoothQuant 都属于 round-to-nearest (RTN) 量化：舍入到最近的定点数。GPT-Q 则是把量化问题视作优化问题，逐层寻找最优的量化权重. 具体可以参考 NLP（十一）：大语言模型的模型量化(INT8/INT4)技术 QLoRA