Natural Language Processing :
Developing programs to understand natural language is important in AI because a natural form of communication with systems is essential for user acceptance. One of the most critical tests for intelligent behavior is the ability to communicate effectively. This was the test proposed by Alan Turing. AI programs must be able to communicate with their human counterparts in a natural way, and natural language is one of the most important mediums for that purpose. A program understands a natural language if it behaves by taking a correct or acceptable action in response to the input. For example, we say a child demonstrates understanding if it responds with the correct answer to a question. The action taken need not be the external response. It may be the creation of some internal data structures. The structures created should be meaningful and correctly interact with the world model representation held by the program. In this chapter we explore many of the important issues related to natural language understanding and language generation.
This chapter explores several techniques that are used to enable humans to interact with computers via natural human languages. Natural languages are the languages used by humans for communication (among other functions). They are distinctly different from formal languages, such as C++, Java, and PROLOG. One of the main differences, which we will examine in some detail in this chapter, is that natural languages are ambiguous, meaning that a given sentence can have more than one possible meaning, and in some cases the correct meaning can be very hard to determine. Formal languages are almost always designed to ensure that ambiguity cannot occur. Hence, a given program written in C++ can have only one interpretation. This is clearly desirable because otherwise the computer would have to make an arbitrary decision as to which interpretation to work with. It is becoming increasingly important for computers to be able to understand natural languages. Telephone systems are now widespread that are able to understand a narrow range of commands and questions to assist callers to large call centers, without needing to use human resources. Additionally, the quantity of unstructured textual data that exists in the world (and in particular, on the Internet) has reached unmanageable proportions. For humans to search through these data using traditional techniques such as Boolean queries or the database query language SQL is impractical. The idea that people should be able to pose questions in their own language, or something similar to it, is an increasingly popular one. Of course, English is not the only natural language. A great deal of research in natural language processing and information retrieval is carried out in English, but many human languages differ enormously from English. Languages such as Chinese, Finnish, and Navajo have almost nothing in common with English (although of course Finnish uses the same alphabet). Hence, a system that can work with one human language cannot necessarily deal with any other human language. In this section we will explore two main topics. First, we will examine natural language processing, which is a collection of techniques used to enable computers to “understand” human language. In general, they are concerned with extracting grammatical information as well as meaning from human utterances but they are also concerned with understanding those utterances, and performing useful tasks as a result. Two of the earliest goals of natural language processing were automated translation (which is explored in this chapter) and database access. The idea here was that if a user wanted to find some information from a database, it would
make much more sense if he or she could query the database in her language, rather than needing to learn a new formal language such as SQL. Information retrieval is a collection of techniques used to try to match a query (or a command) to a set of documents from an existing corpus of documents. Systems such as the search engines that we use to find data on the Internet use information retrieval (albeit of a fairly simple nature).
Overview of linguistics
In dealing with natural language, a computer system needs to be able to process and manipulate language at a number of levels.
Phonology. This is needed only if the computer is required to understand spoken language. Phonology is the study of the sounds that make up words and is used to identify words from sounds. We will explore this in a little more detail later, when we look at the ways in which computers can understand speech.
Morphology. This is the first stage of analysis that is applied to words, once they have been identified from speech, or input into the system. Morphology looks at the ways in which words break down into components and how that affects their grammatical status. For example, the letter “s” on the end of a word can often either indicate that it is a plural noun or a third-person present-tense verb.
Syntax. This stage involves applying the rules of the grammar from the language being used. Syntax determines the role of each word in a sentence and, thus, enables a computer system to convert sentences into a structure that can be more easily manipulated.
Semantics. This involves the examination of the meaning of words and sentences. As we will see, it is possible for a sentence to be syntactically correct but to be semantically meaningless. Conversely, it is desirable that a computer system be able to understand sentences with incorrect syntax but that still convey useful information semantically.
Pragmatics. This is the application of human-like understanding to sentences and discourse to determine meanings that are not immediately clear from the semantics. For example, if someone says, “Can you tell me the time?”, most people know that “yes” is not a suitable answer. Pragmatics enables a computer system to give a sensible answer to questions like this.
In addition to these levels of analysis, natural language processing systems must apply some kind of world knowledge. In most real-world systems, this world knowledge is limited to a specific domain (e.g., a system might have detailed knowledge about the Blocks World and be able to answer questions about this world). The ultimate goal of natural language processing would be to have a system with enough world knowledge to be able to engage a human in discussion on any subject. This goal is still a long way off.
In studying the English language, morphology is relatively simple. We have endings such as -ing, -s, and -ed, which are applied to verbs; endings such as -s and -es, which are applied to nouns; we also have the ending -ly, which usually indicates that a word is an adverb.
We also have prefixes such as anti-, non-, un-, and in-, which tend to indicate negation, or opposition.
We also have a number of other prefixes and suffixes that provide a variety of semantic and syntactic information.
In practice, however, morphologic analysis for the English language is not terribly complex, particularly when compared with agglutinative languages such as German, which tend to combine words together into single words to indicate combinations of meaning.
Morphologic analysis is mainly useful in natural language processing for identifying parts of speech (nouns, verbs, etc.) and for identifying which words belong together.
In English, word order tends to provide more of this information than morphology, however. In languages such as Latin, word order was almost entirely superficial, and the morphology was extremely important. Languages such as French, Italian, and Spanish lie somewhere between these two extremes.
As we will see in the following sections, being able to identify the part of speech for each word is essential to understanding a sentence. This can partly be achieved by simply looking up each word in a dictionary, which might contain for example the following entries:
(swims, verb, present, singular, third person)
(swimmer, noun, singular)
(swim, verb, present, singular, first and second persons)
(swim, verb, present plural, first, second, and third persons)
(swam, verb, past)
Clearly, a complete dictionary of this kind would be unfeasibly large. A more practical approach is to include information about standard endings, such as:
(-ed, verb, past)
(-s, noun, plural)
This works fine for regular verbs, such as walk, but for all natural languages there are large numbers of irregular verbs, which do not follow these rules. Verbs such as to be and to do are particularly difficult in English as they do not seem to follow any morphologic rules.
The most sensible approach to morphologic analysis is thus to include a set of rules that work for most regular words and then a list of irregular words.
For a system that was designed to converse on any subject, this second list would be extremely long. Most natural language systems currently are designed to discuss fairly limited domains and so do not need to include over-large look-up tables.
In most natural languages, as well as the problem posed by the fact that word order tends to have more importance than morphology, there is also the difficulty of ambiguity at a word level.
This kind of ambiguity can be seen in particular in words such as trains, which could be a plural noun or a singular verb, and set, which can be a noun, verb, or adjective.
Parsing involves mapping a linear piece of text onto a hierarchy that represents the way the various words interact with each other syntactically.
First, we will look at grammars, which are used to represent the rules that define how a specific language is built up.
Most natural languages are made up of a number of parts of speech, mainly the following:
Verb o Noun
Adjective o Adverb
Conjunction o Pronoun
In fact it is useful when parsing to combine words together to form syntactic groups. Hence, the words, a dog, which consist of an article and a noun, can also be described as a noun phrase.
A noun phrase is one or more words that combine together to represent an object or thing that can be described by a noun. Hence, the following are valid noun phrases: christmas, the dog, that packet of chips, the boy who had measles last year and nearly died, my favorite color
A noun phrase is not a sentence—it is part of a sentence.
A verb phrase is one or more words that represent an action. The following are valid verb phrases: swim, eat that packet of chips, walking
A simple way to describe a sentence is to say that it consists of a noun phrase and a verb phrase. Hence, for example: That dog is eating my packet of chips.
In this sentence, that dog is a noun phrase, and is eating my packet of chips is a verb phrase. Note that the verb phrase is in fact made up of a verb phrase, is eating, and a noun phrase, my packet of chips.
A language is defined partly by its grammar. The rules of grammar for a language such as English can be written out in full, although it would be a complex process to do so.
To allow a natural language processing system to parse sentences, it needs to have knowledge of the rules that describe how a valid sentence can be constructed.
These rules are often written in what is known as Backus–Naur form (also known as Backus normal form—both names are abbreviated as BNF).
BNF is widely used by computer scientists to define formal languages such as C++ and Java. We can also use it to define the grammar of a natural language.
A grammar specified in BNF consists of the following components:
Terminal symbols. Each terminal symbol is a symbol or word that appears in the language itself. In English, for example, the terminal symbols are our dictionary words such as the, cat, dog, and so on. In formal languages, the terminal symbols include variable names such as x, y, and so on, but for our purposes we will consider the terminal symbols to be the words in the language.
Nonterminal symbols. These are the symbols such as noun, verb phrase, and conjunction that are used to define words and phrases of the language. A nonterminal symbol is so-named because it is used to represent one or more terminal symbols.
The start symbol. The start symbol is used to represent a complete sentence in the language. In our case, the start symbol is simply sentence, but in first-order predicate logic, for example, the start symbol would be expression.
Rewrite rules. The rewrite rules define the structure of the grammar. Each rewrite rule details what symbols (terminal or nonterminal) can be used to make up each nonterminal symbol.
Let us now look at rewrite rules in more detail. We saw above that a sentence could take the following form: noun phrase verb phrase
We thus write the following rewrite rule: Sentence→NounPhrase VerbPhrase This does not mean that every sentence must be of this form, but simply that a string of symbols that takes on the form of the right-hand side can be rewritten in the form of the left-hand side. Hence, if we see the words
The cat sat on the mat
we might identify that the cat is a noun phrase and that sat on the mat is a verb phrase. We can thus conclude that this string forms a sentence.
We can also use BNF to define a number of possible noun phrases.
Note how we use the “|” symbol to separate the possible right-hand sides in BNF:
| Article Noun
| Adjective Noun
| Article Adjective Noun
Ø Similarly, we can define a verb phrase: VerbPhrase→ Verb
| Verb NounPhrase
| Adverb Verb NounPhrase
The structure of human languages varies considerably. Hence, a set of rules like this will be valid for one language, but not necessarily for any other language.
For example, in English it is usual to place the adjective before the noun (black cat, stale bread), whereas in French, it is often the case that the adjective comes after the noun (moulin rouge). Thus far, the rewrite rules we have written consist solely of nonterminal symbols.
Rewrite rules are also used to describe the parts of speech of individual words (or terminal symbols):
| Mount Rushmore
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