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Last modified October 28, 1994
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1.1 Why do morphological parsing?
1.2 The two-level model of morphology
1.3 The KIMMO parser
1.4 The PC-KIMMO parser
1.5 Unification-based word grammar
1.6 Englex: a two-level description of English morphology
1.7 Conclusion
References
Figure 1 Main components of Karttunen's KIMMO parser
Figure 2 Parse tree and feature structure for enlargements
Figure 3 Fragment of a word grammar of English
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This chapter describes PC-KIMMO, a morphological parser based on Kimmo
Koskenniemi's model of two-level morphology ( Koskenniemi 1983). While
PC-KIMMO was adequate to decompose a word into morphemes, it was not able
directly to compute the part of speech of a derivationally complex word or
return a word's inflectional features--precisely the information required
for syntactic parsing. These deficiencies have now been remedied by adding a
unification-based word grammar component to version 2 of PC-KIMMO which can
provide parse trees and feature structures. A substantial analysis of
English for use with PC-KIMMO is also described.
1.1 Why do morphological parsing?
---------------------------------
Parsing is a standard technique used in the field of natural language
processing. When you think of parsing, you likely think of syntactic
parsing. But before a syntactic parser can parse a sentence, it must be
supplied with information about each word in the sentence. For instance, to
parse the sentence The cat chased the rat, a parser must know that cat is a
singular noun, chased is a past tense verb, and so on. In English, such
information may be supplied by a lexicon that simply lists all wordforms
with their part of speech and inflectional information such as number and
tense. Because English has a relatively simple inflectional system, the
number of forms that must be listed in such a lexicon is manageable. Note
that countable nouns such as cat have only have two inflected forms,
singular and plural, and regular verbs such as chase have only four
inflected forms: the base form, the -s form, the -ed form, and the -ing
form. But an exhaustive lexical listing is simply not feasible for many
other languages, such as Finnish, Turkish, and Quechua, which may have
hundreds of inflected forms for each noun or verb. For such languages, one
must build a word parser that will use the morphological system of the
language to compute the part of speech and inflectional categories of any
word.
Even for English a morphological parser may be necessary. Although English
has a limited inflectional system, it has very complex and productive
derivational morphology. For example, from the root compute come derived
forms such as computer, computerize, computerization, recomputerize,
noncomputerized, and so on. It is impossible to list exhaustively in a
lexicon all the derived forms (including coined terms or inventive uses of
language) that might occur in natural text.
1.2 The two-level model of morphology
-------------------------------------
A major breakthrough in the field of morphological parsing came in 1983 when
Kimmo Koskenniemi, a Finnish computer scientist, produced his dissertation
Two- level morphology: A general computational model for word-form
recognition and generation ( Koskenniemi 1983). Koskenniemi's model of
two-level morphology was based on the traditional distinction that linguists
make between morphotactics, which enumerates the inventory of morphemes and
specifies in what order they can occur, and morphophonemics, which accounts
for alternate forms or "spellings" of morphemes according to the
phonological context in which they occur. For example, the word chased is
analyzed morphotactically as the stem chase followed by the suffix -ed.
However, the addition of the suffix -ed apparently causes the loss of the
final e of chase; thus chase and chas are allomorphs or alternate forms of
the same morpheme. Koskenniemi's model is "two-level" in the sense that a
word is represented as a direct, letter-for-letter correspondence between
its lexical or underlying form and its surface form. For example, the word
chased is given this two-level representation (where + is a morpheme
boundary symbol and 0 is a null character):
Lexical form: c h a s e + e d
Surface form: c h a s 0 0 e d
For more on the phonological properties of the two-level model, see Antworth
1991.
1.3 The KIMMO parser
--------------------
Shortly after Koskenniemi's dissertation appeared, Lauri Karttunen and others
produced a LISP implementation of Koskenniemi's two-level model and dubbed
it KIMMO ( Karttunen 1983). The main components of the KIMMO parser are
shown in Figure 1. It had two analytical components: the rules component and
the lexical component, or lexicon. First, the rules component consisted of
two-level rules that accounted for regular phonological or orthographic
alternations, such as chase versus chas. Second, the lexicon listed all
morphemes (stems and affixes) in their lexical form and specified
morphotactic constraints. For example, the lexicon would have included
lexical entries for the verb stem chase and the suffix -ed, and would have
specified their relative order. Using these data components were two
processing functions, the Generator and the Recognizer. The Generator would
accept as input a lexical form such as spy+s and return the surface form
spies. The Recognizer would accept as input a surface form such as spies and
return an underlying form divided into morphemes, namely spy+s, plus a gloss
string such as N+PLURAL.
Figure 1 Main components of Karttunen's KIMMO parser
[Image]
1.4 The PC-KIMMO parser
-----------------------
In 1990, the Summer Institute of Linguistics produced PC-KIMMO version 1, an
implementation of the two-level model that closely followed Karttunen's
KIMMO (see Antworth 1990). Written in C, it ran on personal computers such
as IBM PC compatibles and the Macintosh as well as UNIX. PC-KIMMO was quite
good at what it was designed to do--tokenize a word into a sequence of
tagged morphemes. But it had a serious deficiency: it could not directly
determine the part of speech of a word or its inflectional categories. For
example, given the word enlargements, PC-KIMMO could tokenize it into the
sequence of morphemes en+large+ment+s and gloss each morpheme, but it could
not determine that the entire word was a plural noun. This meant that
PC-KIMMO was not adequate to act as a morphological front end to a syntactic
parser--its most desirable application.
1.5 Unification-based word grammar
----------------------------------
In 1993, version 2 of PC-KIMMO was developed specifically to correct this
deficiency. It does so by adding a third analytical component, a word
grammar. The word grammar is a unification-based chart parser (based on the
PATR-II formalism described in Shieber 1986) that provides parse trees and
feature structures. The chart parser was originally designed for syntactic
parsing. Just as a sentence parser produces a parse tree with words as its
leaf nodes, a word parser produces a parse tree with morphemes as its leaf
nodes. When you parse a sentence, it is normally already tokenized into
words (since we put white space between words); but when you parse a word,
you must first tokenize it into morphemes. This tokenizing is done by the
rules and lexicon. When a surface word is submitted to PC-KIMMO's
Recognizer, the rules and lexicon analyze the word into a sequence of
morpheme structures (or possibly more than one sequence if more than one
analysis is found). A morpheme structure consists of a lexical form, its
gloss, its category, and its features. For example, the word enlargements is
tokenized into this sequence of morpheme structures:
Form: en+ large +ment +s
Gloss: VR1+ AJ +NR25 +PL
Cat: PREFIX ROOT SUFFIX INFL
Feat: [from_pos:AJ [pos: AJ [from_pos:V [from_pos:N
to_pos: V aform:!POS] to_pos: N to_pos: N
finite: !-] number:!SG] number: SG
reg: +]
This analysis is then passed to the word grammar, which returns the parse
tree and feature structure shown in Figure 2.
Figure 2 Parse tree and feature structure for enlargements
Word
______|_______
Stem INFL
_____|______ +s
Stem SUFFIX +PL
___|____ +ment
PREFIX Stem +NR25
en+ |
VR1+ ROOT
`large
`large
Word:
[cat: Word
pos: N
number:PL ]
While each node of the tree has a feature structure associated with it, the
feature structure for the top node is the most important, since these are
the features attributable to the entire word. The feature structure for the
word enlargements specifies two features. First, the feature pos has the
value N, meaning that the lexical category (part-of-speech) of the word is
Noun. Second, the feature number has the value PL for plural. If PC-KIMMO
were being called from a syntactic parser, then it would return to the
syntactic parser the word enlargements with these features. In its overall
architecture, version 2 of PC-KIMMO now resembles the morphological parser
described in Ritchie and others 1992. That parser also first tokenizes a
word into morphemes and then parses the morpheme sequence with a
unification-based parser. However, our unification parser differs
considerably from theirs in its implementation.
The word grammar component uses a grammar file written by the user. A
grammar consists of context-free rules and feature constraints. The format
of the grammar closely follows Shieber's PATR-II formalism ( Shieber 1986).
Figure 3 shows a fragment of a word grammar of English.
The first section of the grammar file contains feature abbreviations.
Feature abbreviations can be used either in lexical entries or in grammar
rules and are expanded by "Let" statements. For example, the feature
abbreviation pl is expanded into the feature structure [number: PL].
Figure 3 Fragment of a word grammar of English
;FEATURE ABBREVIATIONS:
Let pl be = PL
Let irreg be = -
Let v/n be = V
= N
= !SG
Let v\aj be = AJ
= V
= !-
;CATEGORY TEMPLATES:
Let N be = ROOT
= N
= !SG
= !+
= !-
Let V be = ROOT
= V
= !-
= !+
Let AJ be = ROOT
= AJ
= !POS
= !+
;Rule 1
Word = Stem INFL
=
=
=
=
=
;Rule 2
Stem_1 = PREFIX Stem_2
=
=
=
=
=
=
;Rule 3
Stem_1 = Stem_2 SUFFIX
=
=
=
=
=
=
;Rule 4
Stem = ROOT
=
=
=
=
=
The second section of the grammar file contains category templates. These are
feature specifications that are attached to lexical categories such as Noun
and Adjective. This greatly reduces the amount of information that must be
stored in the lexicon. For example, the statement Let N be = SG
means that all nouns are assigned singular number. The grammar in Figure 3
actually contains the statement Let N be = !SG. The exclamation
point in !SG means that this is a default value which can be overridden. For
example, the lexical entry for fox does not need to specify that it is
singular; that information is supplied by the category definition of Noun.
However, the lexical entry for mice (an irregular plural) explicitly sets
the feature number feature to PL (plural), thus overriding the default
value.
The third section of the grammar file contains the word grammar rules.
Associated with each rule are feature constraints. A feature constraint
consists of two feature structures which must unify with each other. Feature
constraints have two functions: they constrain the operation of a rule and
they pass features from one node to another up the parse tree. For example,
in rule 1 of Figure 3 the feature constraint =
requires that the pos feature of the Stem node must have the same value as
the from_pos feature of the INFL node in order for the rule to succeed,
while the feature constraint = passes the value of
the to_pos feature from the INFL node to the pos feature of the Word node.
The word enlargements is an especially good example of the power of the word
grammar because of its complex derivational structure. Its root is the
adjective large; the prefix en- forms the verb enlarge; the suffix -ment
forms the noun enlargement; and finally the inflectional suffix s marks it
plural. To accomplish this, each root or stem has a lexical category such as
Noun or Adjective and each affix has a from_pos feature and a to_pos
feature. The from_pos feature specifies the lexical category of the stems to
which it can attach. The to_pos feature specifies the lexical category of
the resulting stem. For instance, the prefix en- has a from_pos of Adjective
and a to_pos of Verb, since it attaches to an adjective such as large and
produces a verb, enlarge. Rule 2 in Figure 3 says that a Stem is composed of
a PREFIX plus a Stem, for instance enlarge = en+large. The first feature
constraint, = , requires that the from_pos
feature of the PREFIX node must have the same value as the pos feature of
the Stem_2 node. Since the from_pos feature of the prefix en- and the pos
feature of the stem large are both AJ, the rule succeeds. The second feature
constraint, = , passes the value of the to_pos
feature from the PREFIX node to the pos feature of the Stem node; that is,
since the to_pos of the prefix en- is V, the pos of the resulting stem
enlarge is also V.
Rule 1 in Figure 3 accounts for the plural suffix -s. The rule simply says
that a word is composed of a stem plus an inflectional element. The first
two feature constraints use the pos feature to ensure that the plural suffix
attaches only to a noun stem and produces a noun word (that is, it does not
change the part of speech of the stem as do the derivational affixes). The
third feature constraint, = , passes the value of
the number feature from the inflectional suffix to the Word node.
1.6 Englex: a two-level description of English morphology
---------------------------------------------------------
PC-KIMMO version 2 can be used with Englex, a two-level description of
English morphology. Englex consists of a set of orthographic rules, a
20,000-entry lexicon of roots and affixes, and a word grammar. With Englex
and PC-KIMMO, you can morphologically parse English words and text.
Practical applications include morphologically preprocessing text for a
syntactic parser and producing morphologically tagged text (see Antworth
1993). Englex can also be used to explore English morphological structure
for purposes of linguistic analysis.
1.6.1 Coverage
--------------
In terms of its coverage of English, Englex has these goals:
To account for all major spelling rules of English.
To account for all productive morphological structure (affixes,
morphotactic constraints, word class conversion, and so on). While a
20,000-entry lexicon sounds small, Englex can actually recognize many
times that number of words because it analyzes productive derivational
morphology. For example, the lexicon contains entries for re-, compute,
-er, -ize, and -ation and can thus recognize any complex word formed
from those parts.
To establish a critical mass of lexical entries that would handle a
large percentage of non-technical, non-specialized English text.
To provide an interface to syntactic parsing. For each input word,
Englex should return its lexical category (part-of-speech) and all
syntactically relevant inflectional categories (such as number and
tense).
1.6.2 Multiple senses and homonyms
----------------------------------
Englex's lexicon is a parsing lexicon, not a full dictionary. In general,
multiple senses of words are not distinguished. For example, there is only
one entry for the adjective fair, ignoring the fact that it has several
senses (including "not stormy", "impartial" and "light-colored"). However
the noun fair meaning "a festival" is considered a homonym and because it is
a different lexical category it is given its own entry in the noun
sublexicon. The larger issue is how to incorporate semantic information in a
PC-KIMMO lexicon. While PC-KIMMO doesn't support a semantics field in
lexical entries, there are other ways you can include semantic information
in entries.
You can place semantic information in the features field; for example,
features for semantic categories such as animate, human, and so on.
You can use glosses that encode semantic information.
You can include a user-defined semantics field in lexical entries;
while it would be ignored by PC-KIMMO, it would be accessible to other
software.
1.6.3 Lexical category conversion
---------------------------------
Many words in English belong to more than one lexical category; for instance,
the word ride can be either verb or noun. Since the verb ride and the noun
ride appear to share the same basic sense, they are derivationally related.
The relation between them is often described as zero derivation or
conversion. In contrast, the verb shed and the noun shed have unrelated
senses and thus are not derivationally related, but merely homonyms. Clearly
homonyms such as verb shed and noun shed should receive separate lexical
entries since they have no lexical relation to each other. But if you posit
separate lexical entries for words related by lexical conversion, you would
not only lose the linguistic generalization that such words are lexically
related but you would also greatly increase the size of the lexicon, since
English has a very large number of such words. Englex handles lexical
category conversion by positing special sublexicons such as N-V for words
that occur as both noun and verb. A word belonging to the N-V sublexicon is
expanded into both noun and verb instances by the following "Let" statement
in the word grammar:
Let N-V be {[N] [V]}
The curly braces indicate a disjunction of the elements [N] and [V], which
in turn are expanded by category templates for N and V (that is, Noun and
Verb).
1.7 Conclusion
--------------
Koskenniemi's original model of a two-level morphological parser,
instantiated as PC-KIMMO version 1, made it possible to decompose complex
words into their constituent morphemes, but it lacked the ability directly
to compute the part of speech of a complex word or to return inflectional
features in a form that could easily be used by a syntactic parser. By
feeding the output of the original parser into a unification-based word
grammar, version 2 of PC-KIMMO can now provide full parse trees and feature
structures for handling part of speech and inflectional categories. A
substantial description of English including rules, lexicon, and word
grammar is also available for use with PC-KIMMO version 2.
References
----------
Antworth, Evan L. 1990. PC-KIMMO: a two-level processor for
morphological analysis. Occasional Publications in Academic Computing
No. 16. Dallas, TX: Summer Institute of Linguistics.
____. 1991. Introduction to two-level phonology. Notes on Linguistics
53:4-18. Dallas, TX: Summer Institute of Linguistics.
____. 1993. Glossing text with the PC-KIMMO morphological parser.
Computers and the Humanities 26:475-484.
Karttunen, Lauri. 1983. KIMMO: a general morphological processor. Texas
Linguistic Forum 22:163-186.
Koskenniemi, Kimmo. 1983. Two-level morphology: a general computational
model for word-form recognition and production. Publication No. 11.
Helsinki: University of Helsinki Department of General Linguistics.
Ritchie, Graeme D., Graham J. Russell, Alan W. Black, and Stephen G.
Pulman. 1992. Computational morphology: practical mechanisms for the
English lexicon. Cambridge, MA: The MIT Press.
Shieber, Stuart M. 1986. An introduction to unification-based
approaches to grammar. CSLI Lecture Notes No. 4. Stanford, CA: Center
for the Study of Language and Information.
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*This chapter is a slight revision of a paper presented at:
North Texas Natural Language Processing Workshop
May 23, 1994
University of Texas at Arlington
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