Chapter: Artificial Intelligence

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Basic parsing techniques

Transition Networks: A transition network is a finite state automaton that is used to represent a part of a grammar.

Basic parsing techniques

 

Transition Networks

 

A transition network is a finite state automaton that is used to represent a part of a grammar.

 

A transition network parser uses a number of these transition networks to represent its entire grammar.

Each network represents one nonterminal symbol in the grammar. Hence, in the grammar for the English language, we would have one transition network for Sentence, one for Noun Phrase, one for Verb Phrase, one for Verb, and so on.

Fig 10.2 shows the transition network equivalents for three production rules.

 

In each transition network, S1 is the start state, and the accepting state, or final state, is denoted by a heavy border. When a phrase is applied to a transition network, the first word is compared against one of the arcs leading from the first state.

If this word matches one of those arcs, the network moves into the state to which that arc points. Hence, the first network shown in Fig 10.2, when presented with a Noun Phrase, will move from state S1 to state S2.

 

If a phrase is presented to a transition network and no match is found from the current state, then that network cannot be used and another network must be tried. Hence, when starting with the phrase the cat sat on the mat, none of the networks shown in Fig 10.2 will be used because they all have only nonterminal symbols, whereas all the symbols in the cat sat on the mat are terminal.Hence, we need further networks, such as the ones shown in Figure 10.2, which deal with terminal symbols.


Transition networks can be used to determine whether a sentence is grammatically correct, at least according to the rules of the grammar the networks represent.

 

Parsing using transition networks involves exploring a search space of possible parses in a depth-first fashion.

 

Let us examine the parse of the following simple sentence:

 

A cat sat.

 

We begin in state S1 in the Sentence transition network. To proceed, we must follow the arc that is labeled NounPhrase. We thus move out of the Sentence network and into the NounPhrase network.

 

The first arc of the NounPhrase network is labeled Noun. We thus move into the Noun network. We now follow each of the arcs in the Noun network and discover that our first word, A, does not match any of them. Hence, we backtrack to the next arc in the NounPhrase network.

This arc is labeled Article, so we move on to the Article transition network. Here, on examining the second label, we find that the first word is matched by the terminal symbol on this arc.

 

We therefore consume the word, A, and move on to state S2 in the Article network. Because this is a success node, we are able to return to the NounPhrase network and move on to state S2 in this network. We now have an arc labeled Noun.

As before, we move into the Noun network and find that our next word, cat, matches. We thus move to state S4 in the NounPhrase network. This is a success node, and so we move back to the Sentence network and repeat the process for the VerbPhrase arc.

It is possible for a system to use transition networks to generate a derivation tree for a sentence, so that as well as determining whether the sentence is grammatically valid, it parses it fully to obtain further information by semantic analysis from the sentence.

This can be done by simply having the system build up the tree by noting which arcs it successfully followed. When, for example, it successfully follows the NounPhrase arc in the Sentence network, the system generates a root node labeled Sentence and an arc leading from that node to a new node labeled NounPhrase.When the system follows the NounPhrase network and

 

identifies an article and a noun, these are similarly added to the tree.

 In this way, the full parse tree for the sentence can be generated using transition networks. Parsing using transition networks is simple to understand, but is not necessarily as efficient or as effective as we might hope for. In particular, it does not pay any attention to potential ambiguities or the need for words to agree with each other in case, gender, or number.


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