=Paper=
{{Paper
|id=None
|storemode=property
|title=Treex - an open-source framework for natural language processing
|pdfUrl=https://ceur-ws.org/Vol-788/paper2.pdf
|volume=Vol-788
|dblpUrl=https://dblp.org/rec/conf/itat/Zabokrtsky11
}}
==Treex - an open-source framework for natural language processing==
https://ceur-ws.org/Vol-788/paper2.pdf
Treex – an open-source framework
for natural language processing?
Zdeněk Žabokrtský
Charles University in Prague, Institute of Formal and Applied Linguistics
Malostranské náměstı́ 25, 118 00 Prague, Czech Republic
zabokrtsky@ufal.mff.cuni.cz
WWW home page: http://ufal.mff.cuni.cz/~zabokrtsky
Abstract. The present paper describes Treex (formerly decision process (such as searching for optimal feature
TectoMT), a multi-purpose open-source framework for de- weights and other model parameters) is then entirely
veloping Natural Language Processing applications. It fa- left to the learning algorithm.
cilitates the development by exploiting a wide range of soft-
Recent developments in NLP show that another
ware modules already integrated in Treex, such as tools for
sentence segmentation, tokenization, morphological analy- paradigm shift might be approaching with unsuper-
sis, part-of-speech tagging, shallow and deep syntax pars- vised and semi-supervised algorithms, which are able
ing, named entity recognition, anaphora resolution, sen- to learn from data without hand-made annotations.
tence synthesis, word-level alignment of parallel corpora, However, such algorithms require considerably more
and other tasks. The most elaborate application of Treex is complex models and for most NLP tasks they have
an English-Czech machine translation system with trans- not outperformed supervised solutions based on hand-
fer on deep syntactic (tectogrammatical) layer. Besides re- annotated data so far.
search, Treex is used for teaching purposes and helps stu-
Nowadays, researched NLP tasks range from rel-
dents to implement morphological and syntactic analyzers
of foreign languages in a very short time.
atively simple ones (like sentence segmentation, lan-
guage identification), through tasks which already
need a higher level of abstraction (such as morpholog-
1 Introduction ical analysis, part-of-speech tagging, parsing, named
entity recognition, coreference resolution, word sense
Natural Language Processing (NLP) is a multidisci- disambiguation, sentiment analysis, natural language
plinary field combining computer science, mathemat- generation), to highly complex systems (machine
ics and linguistics, whose main aim is to allow com- translation, automatic summarization, or question an-
puters to work with information expressed in human swering). The importance of (and demand for) such
(natural) language. tasks increases along with the rapidly growing amount
The history of NLP goes back to 1950s. Early NLP of textual information available on the Internet.
systems were based on hand-written rules founded by Many NLP applications exploit several NLP mod-
linguistic intuitions. However, roughly two decades ago ules chained in a pipeline (such as a sentence seg-
the growing availability of language data (especially menter and part-of-speech tagger prior to a parser).
textual corpora) and increasing capabilities of com- However, if state-of-the-art solutions created by dif-
puter systems lead to a revolution in NLP: the field ferent authors – often written in different program-
became dominated by data-driven approaches, often ming languages, with different interfaces, using dif-
based on probabilistic modeling and machine learning. ferent data formats and encodings – are to be used,
In such data-driven scenario, the role of hu- a significant effort must be invested into integrating
man experts was moved from designing rules rather to the tools. Even if these issues are only of technical na-
(i) preparing training data enriched with linguistically ture, in real research they constitute one of limiting
relevant information (usually by manual annotation), factors for building more complex NLP applications.
(ii) choice of an adequate probabilistic model, propos- We try to eliminate such problems by introducing
ing features (various indicators potentially useful for a common NLP framework that integrates a number
making the desired predictions), and (iii) specifying an of NLP tools and provides them with unified object-
objective (evaluation) function. Optimization of the oriented interfaces, which hide the technical issues
?
The presented research is supported by the grants from the developer of a larger application. The frame-
MSM0021620838 and by the European Commission’s work’s architecture seems viable – tens of researchers
7FP grant agreement n◦ 231720 (EuroMatrix Plus). We and students have already contributed to the system
would like to thank Martin Popel for useful comments and the framework has been already used for a number
on the paper. of research tasks carried out at the Institute of For-
8 Zdeněk Žabokrtský
mal and Applied linguistics as well as at some other 2. analytical layer (a-layer)
research institutions. The most complex application Each sentence is represented as a shallow-syntax
implemented within the framework is English-Czech dependency tree (a-tree). There is one-to-one cor-
machine translation. The framework is called Treex.1 respondence between m-layer tokens and a-layer
The remainder of the paper is structured as fol- nodes (a-nodes). Each a-node is annotated with
lows. Section 2 overviews related work that had to be the so-called analytical function, which represents
taken into account when developing such framework. the type of dependency relation to its parent
Section 3 presents the main design decisions Treex (i.e. its governing node).
is build on. English-Czech machine translation imple- 3. tectogrammatical layer (t-layer)
mented in Treex is described in Section 4, while other Each sentence is represented as a deep-syntax de-
Treex applications are mentioned in Section 5, which pendency tree (t-tree). Autosemantic (meaning-
also concludes. ful) words are represented as t-layer nodes
(t-nodes). Information conveyed by functional
words (such as auxiliary verbs, prepositions and
2 Related work subordinating conjunctions) is represented by
attributes of t-nodes. Most important attributes
2.1 Theoretical background of t-nodes are: tectogrammatical lemma, functor
(which represents the semantic value of syntactic
Natural language is an immensely complicated phe- dependency relation) and a set of grammatemes
nomenon. Modeling the language in its entirety would (e.g. tense, number, verb modality, deontic modal-
be extremely complex, therefore its description is of- ity, negation).
ten decomposed into several subsequent layers (levels). Edges in t-trees represent linguistic dependencies
There is no broadly accepted consensus on details con- except for several special cases, the most notable
cerning the individual levels, however, the layers typ- of which are paratactic structures (coordinations).
ically roughly correspond to the following scale: pho-
netics, phonology, morphology, syntax, semantics, and
All three layers of annotation are described in an-
pragmatics.
notation manuals distributed with PDT 2.0.
One of such stratificational hypotheses is Func-
This annotation scheme has been adopted and fur-
tional Generative Description (FGD), developed by
ther modified in Treex. One of the modifications con-
Petr Sgall and his colleagues in Prague since the
sists in merging m-layer and a-layer sentence represen-
1960s [18]. FGD was used with certain modifications as
tations into a single data structure.2
the theoretical framework underlying the Prague De-
pendency Treebank [6], which is a manually annotated Treex also profits from the technology developed
corpus of Czech newspaper texts from the 1990s. PDT during the PDT project, especially from the existence
in version 2.0 (PDT 2.0) adds three layers of linguistic of the highly customizable tree editor TrEd, which is
annotation to the original texts: used as the main visualization tool in Treex, and from
the XML-based file format PML (Prague Markup Lan-
1. morphological layer (m-layer) guage, [14]), which is used as the main data format in
Each sentence is tokenized and each token is an- Treex.
notated with a lemma (basic word form, such as
nominative singular for nouns) and morphological
2.2 Other NLP frameworks
tag (describing morphological categories such as
part of speech, number, and tense).
Treex is not the only existing general NLP frame-
1
The framework was originally called TectoMT since work. We are aware of the following other frameworks
starting its development in autumn 2005 [23], because (a more detailed comparison can be found in [15]):
one of the sources of motivation for building the frame-
work was developing a Machine translation (MT) system – ETAP-3 [1] is a C/C++ closed-source NLP frame-
using tectogrammatical (deep-syntactic) sentence repre- work for English-Russian and Russian-English
sentation as the transfer medium. However, MT is by translation, developed in the Russian Academy of
far not the only application of the framework. As the Sciences.
name seemed to be rather discouraging for those NLP
2
developers whose research interests did not overlap with As mentioned above, their units are in a one-to-one rela-
tectogrammatics nor with MT, TectoMT was rebranded tion anyway; merging the two structures together has led
to Treex in spring 2011. To avoid confusion, the name to a significant reduction of time and memory require-
Treex is used throughout the whole text even if it refers ments when processing large data, as well as to a lower
to a more distant history. burden for eyes when browsing the structures.
Treex – an open-source framework for NLP 9
– GATE (Java, LGPL) is one of the most widely during the translation process are made by statistical
used NLP frameworks with integrated graphical models like in SMT, not by rules.
user interface. It is being developed at University
of Sheffield [4].
– Apache OpenNLP (Java, LGPL)3 is an organiza- 3 Treex architecture overview
tional center for open source NLP projects.
3.1 Basic design decisions
– WebLicht4 is a Service Oriented Architecture for
building annotated German text corpora. The architecture of Treex is based on the following
– Apertium [20] is a free/open-source machine trans- decisions:
lation platform with shallow transfer.
– Treex is primarily developed in Linux. However,
In our opinion, none of these frameworks seems fea- platform independent solutions are searched for
sible (or mature enough) for experiments on MT based wherever possible.
on deep-syntactic dependency transfer. The only ex- – The main programming language of Treex is Perl.
ception is ETAP-3, whose theoretical assumptions are However, a number of tools written in other lan-
similar to that of Treex (its dependency-based stratifi- guages have been integrated into Treex (after pro-
cational background theory called Meaning-Text The- viding them with a Perl wrapper).
ory [13] bears several resemblances to FGD), however, – Linguistic interpretability – data structures repre-
it is not an open-source project. senting natural language sentences in Treex must
be understandable by a human (so that e.g. trans-
lation errors can be traced back to their source).
2.3 Contemporary machine translation
Comfortable visualization of the data structures is
MT is a notoriously hard problem and it is studied supported.
by a broad research field nowadays: every year there – Modularity – NLP tools in Treex are designed so
are several conferences, workshops and tutorials dedi- that they are easily reusable for various tasks (not
cated to it (or even to its subfields). It goes beyond the only for MT),
scope of this work even to mention all the contempo- – Rules-vs-statistics neutrality – Treex architecture
rary approaches to MT, but several elaborate surveys is neutral with respect to the rules vs. statistics
of current approaches to MT are already available to opposition (rule-based as well as statistical solu-
the reader elsewhere, e.g. in [10]. tions are combined).
A distinction is usually made between two MT – Massive data – Treex must be capable of process-
paradigms: rule-based MT (RBMT) and sta- ing large data (such as millions of sentence pairs
tistical MT (SMT). The rule-based MT systems are in parallel corpora), which implies that distributed
dependent on the availability of linguistic knowledge processing must be supported.
(such as grammar rules and dictionaries), whereas sta- – Language universality – ideally, Treex should be
tistical MT systems require human-translated parallel easily extendable to any natural language.
text, from which they extract the translation knowl- – Data interchange support – XML is used as the
edge automatically. One of the representatives of the main storage format in Treex, but Treex must be
first group is the already mention system ETAP-3. able to work with a number of other data formats
Nowadays, the most popular representatives of the used in NLP.
second group are phrase-based systems (in which the
term ‘phrase’ stands simply for a sequence of words, 3.2 Data structure units
not necessarily corresponding to phrases in constituent
syntax), e.g. [8], derived from the IBM models [3]. In Treex, representations of a text in a natural lan-
Even if phrase-based systems have more or less guage is structured as follows:
dominated the field in the recent years, their trans-
– Document. A Treex document is the smallest in-
lation quality is still far from perfect. Therefore we
dependently storable unit. A document represents
believe it makes sense to investigate also alternative
a piece of text (or several parallel pieces of texts
approaches.
in the case of multilingual data) and its linguistic
MT implemented in Treex lies somewhere between
representations. A document contains an ordered
the two main paradigms. Like in RBMS, sentence rep-
sequence of bundles.
resentations used in Treex are linguistically in-
– Bundle. A bundle corresponds to a sentence (or
terpretable. However, the most important decisions
a tuple of sentences in the case of parallel data)
3 and its linguistic representations. A bundle con-
http://opennlp.sourceforge.net
4
http://weblicht.sfs.uni-tuebingen.de/englisch/index.shtml tains a set of zones.
10 Zdeněk Žabokrtský
– Zone. Each language (languages are distinguished (a) Simple Treex scenario:
using ISO 639-2 codes in Treex) can have one or Util::SetGlobal language=en # do everyth. in English zone
more zones in a bundle.5 Each zone corresponds to Block::Read::Text
W2A::Segment
# read a text from STDIN
# segment it into sentences
one particular sentence and at most one tree for W2A::Tokenize # divide sentences into words
each layer of linguistic description. W2A::EN::TagMorce # morphological tagging
W2A::EN::Lemmatiz # lemmatization (basic word forms)
– Tree. All sentence representations in Treex have W2A::EN::ParseMST # dependency parsing
the shape of an oriented tree.6 At this moment W2A::EN::SetAfunAuxCPCoord
W2A::EN::SetAfun
# fill analytical functions
# fill analytical functions
there are four types of trees: (1) a-trees – morphol- Write::CoNLLX # print trees in CoNLLX format
ogy and surface-dependency (analytical) trees, Write::Treex # store trees into XML file
(2) t-trees – tectogrammatical trees, (3) p-trees – (b) Input text example:
phrase-structure (constituency) trees, (4) n-trees
– trees of named entities. When the prince mentions the rose, the geographer explains
that he does not record roses, calling them "ephemeral".
– Node. Each nodes contains (is labeled by) a set of The prince is shocked and hurt by this revelation. The
attributes (name-value pairs). geographer recommends that he visit the Earth.
– Attribute. Some node attributes are universal
(c) Fragment from the printed output (simplified):
(such as identifier), but most of them are specific
for a certain layer. The set of attribute names and 1 The the DT 2
their values for a node on a particular layer is de- 23 prince
is
prince
be
NN
VBZ
3
0
clared using the Treex PML schema.7 Attribute 4 shocked shock VBN 5
values can be further structured. 5 and and CC 3
6 hurt hurt VBN 5
7 by by IN 5
Of course, there are also many other types of data 8 this this DT 9
9 revelation revelation NN 7
structures used by individual integrated modules (such 10 . . . 3
as dictionary lists, weight vectors and other trained
parameters, etc.), but they are usually hidden behind (d) A-tree visualization in TrEd:
module interfaces and no uniform structure is required
for them. a-tree
zone=en
3.3 Processing units
is
Pred
There are two basic levels of processing units in Treex: VBZ
– Block. Blocks are the smallest processing units in- prince and .
Sb NR AuxG
dependently applicable on a document. NN CC .
– Scenario. Scenarios are sequences of blocks. When
a scenario is applied on a document, the blocks The shocked hurt by
from the sequence are applied on the document AuxA NR NR AuxP
DT VBN VBN IN
one after another.
revelation
Adv
NN
this
Atr
5 DT
Having more zones per language is useful e.g. for com-
paring machine translation with reference translation,
or translation outputs from several systems. Moreover
Fig. 1. Simple scenario for morphological and surface-
it highly simplifies processing of parallel corpora, or
syntactic analysis of English texts. Generated trees are
comparisons of alternative implementations of a certain
printed in the CoNLLX format, which is a simple line-
tasks (such as different dependency parsers).
6 oriented format for representing dependency trees.
However, tree-crossing edges such as anaphora links in
a dependency tree can be represented too (as node at-
tributes).
7
There are also “wild” attributes allowed, which can store
any Perl data structure without its prior declaration
by PML. However, such undeclared attributes should
serve only for tentative or rapid development purposes,
as they cannot be validated.
Treex – an open-source framework for NLP 11
A block can change a document’s content “in 4 English-Czech machine translation
place”8 via a predefined object-oriented interface. One in Treex
can distinguish several broad categories of blocks:
– blocks for sentence analysis – blocks for tokeniza- The translation scenario implemented in Treex com-
tion, morphological tagging, parsing, anaphora poses of three steps described in the following sec-
resolution, etc. tions: (1) analysis of the input sentences up to tec-
– blocks for sentence synthesis – blocks for propa- togrammatical layer of abstraction, (2) transfer of the
gating agreement categories, ordering words, in- abstract representation to the target language, and
flecting word forms, adding punctuation, etc. (3) synthesis (generating) of sentences in the target
– blocks for transfer – blocks for translating a com- language. See an example in Figure 2.
ponent of a linguistic representation from one lan-
guage to another, etc.
– blocks for parallel texts – blocks for word align- 4.1 Analysis
ment, etc.
– writer and reader blocks – block for stor-
ing/loading Treex documents into/from files or The analysis step can be decomposed into three phases
other streams (in the PML or other format),9 corresponding to morphological, analytical and tec-
– auxiliary blocks – blocks for testing, printing, etc. togrammatical analysis.
In the morphological phase, a text to be trans-
If possible, we try to implement blocks in a lan- lated is segmented into sentences and each sentence
guage independent way. However, many blocks will re- is tokenized (segmented into words and punctuation
main language specific (for instance a block for moving marks). Tokens are tagged with part of speech and
clitics in Czech clauses can hardly be reused for any other morphological categories by the Morce tag-
other language). ger [19], and lemmatized.
There are large differences in complexity of blocks. In the analytical phase, each sentence is parsed us-
Some blocks contain just a few simple rules (such as ing the dependency parser [12] based on Maximum
regular expressions for sentence segmentation), while Spanning Tree algorithm, which results in an analyti-
other blocks are Perl wrappers for quite complex prob- cal tree for each sentence. Tree nodes are labeled with
abilistic models resulting from several years of research analytical functions (such as Sb for subject, Pred for
(such as blocks for parsing). predicate, and Adv for adverbial).
As for block granularity, there are no widely agreed Then the analytical trees are converted to the tec-
conventions for decomposing large NLP applica- togrammatical trees. Each autosemantic word with its
tions.10 We only follow general recommendations for associated functional words is collapsed into a sin-
system modularization. A piece of functionality should gle tectogrammatical node, labeled with lemma, func-
be performed by a separate block if it has well defined tor (semantic role), formeme,11 and semantically in-
input and output states of Treex data structures, if it dispensable morphologically categories (such as tense
can be reused in more applications and/or it can be with verbs and number with nouns, but not number
(at least potentially) replaced by some other solution. with verbs as it is only imposed by subject-predicate
agreement). Coreference of pronouns is also resolved
and tectogrammatical nodes are enriched with infor-
mation on named entities (such as the distinction be-
8 tween location, person and organization) resulting
Pipeline processing (like with Unix text-processing com-
mands) is not feasible here since linguistic data are from Stanford Named Entity Recognizer [5].
deeply structured and the price for serializing the data
11
at each boundary would be high. Formemes specify how tectogrammatical nodes are re-
9
In the former versions, format converters were consid- alized in the surface sentence shape. For instance,
ered as tools separated from scenarios. However, provid- n:subj stands for semantic noun in the subject posi-
ing the converters with the uniform block interface al- tion, n:for+X for semantic noun with preposition for,
lows to read/write data directly within a scenario, which v:because+fin for semantic verb in a subordinating
is not only more elegant, but also more efficient (inter- clause introduced by the conjunction because, adj:attr for
mediate serialization and storage can be skipped). semantic adjective in attributive position. Formemes do
10
For instance, some taggers provides both morphological not constitute a genuine tectogrammatical component
tag and lemma for each word form, while other taggers as they are not oriented semantically (but rather mor-
must be followed by a subsequent lemmatizer in order phologically and syntactically). However, they have been
to achieve the same functionality. added to t-trees in Treex as they facilitate the transfer.
12 Zdeněk Žabokrtský
Fig. 2. Analysis-transfer-synthesis translation scenario in Treex applied on the English sentence “However, this very
week, he tried to find refuge in Brazil.”, leading to the Czech translation “Přesto se tento právě týden snažil najı́t
útočiště v Brazı́lii.”. Thick edges indicate functional and autosemantic a-nodes to be merged.
4.2 Transfer labelling is then revealed by the tree-modified Viterbi
algorithm [22].
The transfer phase follows, whose most difficult part Originally, we estimated the translation model sim-
consists in labeling the tree with target-language lem- ply by using pair frequencies extracted from English-
mas and formemes. Changes of tree topology and of Czech parallel data. A significant improvement was
other attributes12 are required relatively infrequently. reached after replacing such model by Maximum En-
Our model for choosing the right target-language tropy model. In the model, we employed a wide range
lemmas and formemes in inspired by Noisy Channel of features resulting from the source-side analysis. The
Model which is the standard approach in the contem- weights were optimized using training data extracted
porary SMT and which combines a translation model from the CzEng parallel treebank [2], which contains
and a language model of the target language. In other roughly 6 million English-Czech pairs of analyzed and
words, one should not rely only on the information aligned sentences.
on how faithfully the meaning is transfered by some
translation equivalent, but also the additional model
4.3 Synthesis
can be used which estimates how well some translation
equivalent fits to the surrounding context.13 Finally, surface sentence shape is synthesized from the
Unlike in the mainstream SMT, in tectogrammat- tectogrammatical tree, which is basically a reverse
ical transfer we do not use this idea for linear struc- operation for the tectogrammatical analysis: adding
tures, but for trees. So the translation model estimates punctuation and functional words, spreading morpho-
the probability of source and target lemma pair, while logical categories according to grammatical agree-
the language tree model estimates the probability of ment, performing inflection (using Czech morphology
a lemma given its parent. The globally optimal tree database [7]), arranging word order etc.
12
For instance, number of nouns must be changed to plural
if the selected target Czech lemma is a plurale tantum. 4.4 Evaluating translation quality
Similarly, verb tense must be predicted if an English
infinitive or gerund verb form is translated to a finite There are two general methods for evaluating transla-
verb form. tion quality of outputs of MT systems: (1) the quality
13
This corresponds to the intuition that translating to can be judged by humans (either using a set of criteria
one’s native language is simpler for a human than trans- such as grammaticality and intelligibility, or relatively
lating to a foreign language. by comparing outputs of different MT systems), or
Treex – an open-source framework for NLP 13
!
"
! !#
'
$%&
Fig. 3. Tectogrammatical transfer implemented as Hidden Markov Tree Model.
(2) the quality can be estimated by automatic met- – linguistic data processing service for other research
rics, which usually measure some form of string-wise carried out in other institutions, such as data anal-
overlap of an MT system’s output with one or more yses for prosody prediction for The University of
reference (human-made) translations. West Bohemia [17].
Both types of evaluation are used regularly during
the development of our MT system. Automatic metrics Treex significantly simplifies code sharing across
are used after any change of the translation scenario, individual research projects in our institute. There are
as they are cheap and fast to perform. Large scale around 15 programmers (postgraduate students and
evaluations by volunteer judges are organized annu- researchers) who have significantly contributed to the
ally as a shared task with the Workshop on Statistical development of Treex in the last years; four of them
Machine Translation.14 Performance of the tectogram- are responsible for developing the central components
matical translation increases every year in both mea- of the framework infrastructure called Treex Core.
sures, and it already outperforms some commercial as Last but not least, Treex is used for teaching pur-
well as academic systems. Actually, it is the participa- poses in our institute. Undergraduate students are
tion in this shared task (a competition, in other words) supposed to develop their own modules for morpho-
what provides the strongest motivation momentum for logical and syntactic analysis for foreign languages of
Treex developers. their choice. Not only that the existence of Treex en-
ables the students to make very fast progress, but their
contributions are accumulated in the Treex Subver-
5 Final remarks and conclusions sion repository too, which enlarges the repertory of
languages treatable by Treex.15
Even if tectogrammatical translation is considered as
the main application of Treex, Treex has been used for There are two main challenges for the Treex devel-
a number of other research purposes as well: opers now. The first challenge is to continue improving
the tectogrammatical translation quality by better ex-
– other MT-related tasks – Treex has been used for ploitation of the training data. The second challenge
developing alternative MT quality measures in [9], is to widen the community of Treex users and devel-
and for improving outputs of other MT systems by opers by distributing majority of Treex modules via
grammatical post-processing in [11], CPAN (Comprehensive Perl Archive Network), which
– building linguistic data resources – Treex has been is a broadly respected repository of Perl modules.
employed in the development of resources such When thinking about a more distant future of MT
as the Prague Czech-English Dependency Tree- and NLP in general, an exciting question arises about
bank [21], the Czech-English parallel corpus the future relationship of linguistically interpretable
CzEng [2], and Tamil Dependency Treebank [16]. 15
There are modules for more than 20 languages available
14
http://www.statmt.org/wmt11/ in Treex now.
14 Zdeněk Žabokrtský
approaches (like that of Treex) and purely statisti- 12. R . McDonald, F. Pereira, K. Ribarov, and J. Hajič:
cal phrase-based approaches. Promising results of [11], Non-projective dependency parsing using spanning tree
which uses Treex for improving the output of a phrase- algorithms. In Proceedings of Human Langauge Tech-
based system and thus reaches the state-of-the-art MT nology Conference and Conference on Empirical Meth-
quality in English-Czech MT, show that combinations ods in Natural Language Processing, pp. 523–530,
Vancouver, BC, Canada, 2005.
of both approaches might be viable.
13. I.A. Mel’čuk: Dependency syntax: theory and practice.
State University of New York Press, 1988.
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