Role of Unification & Realization in Natural Language Generation

1. Role of Unification &
Realization Natural
Language Generation.
RISHAV BHURTEL 19MTRDS008

2. What is NLG?
 Natural Language Generation (NLG), is the “process of producing meaningful
phrases and sentences in the form of natural language.” In its essence, it
automatically generates narratives that describe, summarize or explain input
structured data in a human-like manner at the speed of thousands of pages per
second.
 In general terms, NLG (Natural Language Generation) and NLU (Natural Language
Understanding) are subsections of a more general NLP domain that encompasses
all software which interprets or produces human language, in either spoken or
written form
 NLG software can write, it can’t read. The part of NLP that reads human language
and turns its unstructured data into structured data understandable to computers
is called Natural Language Understanding.

3. Realization
 In linguistic, realization is the process by which some kind of surface
representation is derived from its underlying representation; that is, the way in
which some abstract object of linguistic analysis comes to be produced in actual
language. Phonemes are often said to be realized by speech sounds. The different
sounds that can realize a particular phoneme are called its allophones.
 Realization is also a subtask of natural language generation, which involves
creating an actual text in a human language (English, French, etc.) from a syntactic
representation. There are a number of software packages available for realization,
most of which have been developed by academic research groups in NLG.

4.  It involves the approaches used to generate syntactically and semantically valid
text, given an abstract linguistic representation. Based on the data and nature of
data, a typical task generation includes text-to-text generation, database-to-text
generation, concept-to-text generation, and speech-to-text generation

5.  Realisation involves three kinds of processing:
 Syntactic realisation: Using grammatical knowledge to choose inflections, add
function words and also to decide the order of components. For example, in
English the subject usually precedes the verb, and the negated form of smoke is
do not smoke.
 Morphological realisation: Computing inflected forms, for example the plural
form of woman is women (not womans).
 Orthographic realisation: Dealing with casing, Punctuation , and formatting. For
example, capitalising The because it is the first word of the sentence.

6.  behaviour realizer : a behaviour realizer is basically a behaviour generator, often
based on behaviour mark-up language.
 chart realizer : a chart realizer, such as OpenCCG, produces sentences from logical
forms as input.
 linguistic realizer : a linguistic realizer produces natural language text.
 multimodal realizer .. a multimodal realizer generates both verbal and nonverbal
behaviour in virtual humans, for instance.

7. Cont.…
 sentence realizer .. a sentence realizer produces sentences from syntax or logical
forms.
 surface realizer .. a surface realizer generates text from the abstract,
syntactic representations of each sentence.

8. Unification
 Unification is an operation that
 Merges the information of two structures
 Rejects the merging of incompatible structures
 Simple Unification
[NUMBER SG] |_| [NUMBER SG] = [NUMBER SG]
[NUMBER SG] |_| [NUMBER PL] Fails!
[NUMBER SG] |_| [NUMBER [ ] ] = [NUMBER SG]
where [ ] means unspecified value.
[NUMBER SG] |_| [PERSON 3 ] =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑆𝐺
𝑃𝑒𝑟𝑠𝑜𝑛 3

9.  Formally unification is defined as the most general feature structure H such that F
⊑ H, G ⊑ H. The unification operation is monotonic. This means that if a feature
structure satisfies some description, unifying with another FS results in a new FS
that still satisfies the original description (i.e. all of the original information is
retained).
 A direct consequence of the above is that unification is order-independent.
Regardless of the order in which we unify a number of FSs the final result will be
the same.

10. Unification Parsing
 A different approach to parsing using unification is to consider the grammatical
category as a feature and implement the context-free rule as a unification between
CAT features. E.g.
X0->X1X2
< X0 CAT>=S, < X1 CAT>=NP, < X2 CAT>=VP
< X1 HEAD AGREEMENT>=< X2 HEAD AGREEMENT>
< X2 HEAD >= < X0 HEAD >
 This approach models in an elegant way rules that can be generalized across many
different grammatical categories.
X0->X1 and X 2
< X1 CAT> = < X2 CAT>
< X0 CAT> = < X1 CAT>

11. THANK YOU