Towards a Principled Computational System of Syntactic Ambiguity
Detection and Representation
Hilton Alers-Valentín
, Carlos G. Rivera-Velázquez
, J. Fernando Vega-Riveros
and Nayda G. Santiago
Linguistics and Cognitive Science Program, Department of Hispanic Studies, University of Puerto Rico-Mayagüez,
P.O. Box 6000, Mayagüez, 00681-6000, Puerto Rico
Department of Electrical and Computer Engineering, University of Puerto Rico-Mayagüez, P.O. Box 6000, Mayagüez,
00681-6000, Puerto Rico
Keywords: Syntax, Parser, Lexicon, Structural Ambiguity, Computational Linguistics, Natural Language Processing.
Abstract: This paper presents the current status of a research project in computational linguistics/natural language
processing whose main objective is to develop a symbolic, principle-based, bottom-up system in order to
process and parse sequences of lexical items as declarative sentences in English. For each input sequence, the
parser should produce (maximally) binary trees as generated by the Merge operation on lexical items. Due to
parametric variations in the algorithm, the parser should be able to output (up to four) grammatically feasible
structural representations accounted by alternative constituent analyses because of structural ambiguities in
the parsing of the input string. Finally, the system should be able to state whether a particular string of lexical
items is a possible sentence in account of its parsability. The system has a scalable software framework that
may be suitable for the analysis of typologically-diverse natural languages.
Natural language parsing is a computational process
that takes as input a sequence of words and yields a
syntactic structure for said sequence according to
some sort of procedure. The production of a syntactic
structure from a sequence determines whether it
legally belongs to a language. There are two main
types of parsers used to analyse word sequences. On
one hand, there are probabilistic parsers which, given
a statistical model of the syntactic structure of a
language, will produce the most likely parse of a
sentence, even if the word sequence is actually judged
as ungrammatical by native speakers. Probabilistic
parsers are widely used in natural language
processing applications. However, they require a
manually annotated corpus, a statistical learning
algorithm, as well as training. Although these parsers
are particularly good in identifying syntactic
categories or parts of speech and have a desirable
cost-benefit relation between accuracy and speed,
they have been found rather ineffective in the
representation of sentences containing relative
clauses or long distance relations among constituents.
Deterministic parsers, on the other hand, use a system
of syntactic rules to produce a structural
representation. Deterministic parsers take input
strings of natural languages and analyse them using
production rules of a context free grammar. If, for a
given sequence of lexical items, the rules of a
language grammar cannot produce a structural
representation, the sequence is considered
ungrammatical for that language.
A single grammatical sequence, however, may
have multiple representations if it is syntactically
ambiguous. The (generally assumed) Principle of
Compositionality states that the meaning of an
expressions is a function of the meaning of its parts
and of the way they are syntactically combined
(Partee, 2004). As a consequence, syntactically
ambiguous sequences may also be semantically
ambiguous. There are two main causes of syntactic
ambiguity: referential and structural. Referential
ambiguity is due to possible valuations and
interpretations of noun phrases and pronouns, as it
happens with the three possible assignments of the
possessive pronoun in the woman said that she kicked
her lover. Structural ambiguity occurs when there
exist multiple structural relations between the lexical
Alers-Valentín, H., Rivera-Velázquez, C., Vega-Riveros, J. and Santiago, N.
Towards a Principled Computational System of Syntactic Ambiguity Detection and Representation.
DOI: 10.5220/0007698709800987
In Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019), pages 980-987
ISBN: 978-989-758-350-6
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
items and constituents of a sentence, as in the
classical example the boy saw a man with a telescope.
Sometimes, structural ambiguity is caused by
multiple syntactic category assignment to a lexical
item, as can be seen in visiting relatives can be a
nuisance, in which the first word can be tagged either
as a transitive verb or as an adjective. In this paper, a
computational system is described that detects
syntactic ambiguity in a string and yields the
correspondent structural representations.
For most probabilistic parsers, syntactic
ambiguity, even ungrammaticality, remains
undetected. To deal with structural ambiguity, we
propose a deterministic (symbolic) parser that
produces X-bar structural representations based on
Principle-and-Parameters Theory modules to
generate multiple syntactic parses for syntactically
ambiguous sentences. Deterministic parsers in the
form of minimalist grammars have been already
formalized (Stabler, 1997, 2011; Collins and Stabler,
2016). Other symbolic parsers have been developed
as computational models of syntactic competence
(Berwick, 1985; Fong, 1991, 2005; Chesi, 2004,
2012); however, the parser we propose implements
variation parameters that may account for structural
Principles and Parameters Theory (Chomsky, 1981,
1995) is a generative-derivational theory of the
human Faculty of Language. According to this
theory, a natural (I-)language is an internal,
individual, intensional cognitive state of the human
mind (hence a mental organ) whose initial state,
known as Universal Grammar (UG), contains a set of
invariable principles and constraints that apply to all
languages, as well as a set of variable parameters
(possibly binary-valued) that children set during
language acquisition from the primary linguistic data
to which they are exposed. Among the fundamental
principles of UG are the Structural Dependency
Principle (syntactic structures show hierarchical
structure and non-linear dependencies) and the
Projection Principle (every minimal category projects
its features to a maximal or phrase-level projection).
Some of the best studied syntactic parameters are the
Null Subject Parameter (languages may allow or
disallow null subjects in finite clauses) and the Head
Parameter (syntactic heads can be linearized before or
after their complements). Languages, as steady states
in the development of the Faculty of Language, are
computational cognitive systems consisting of a
lexicon, that contains representations of all primitives
of linguistic computation (along with their features),
and a grammar, a combinatorial system of operations
on these representations. Sequences that satisfy all
grammatical constraints of a language are mapped to
(at least) one tuple of syntactic levels of structural
representations: the theory-internal levels of what has
been known as Deep and Surface Structure (DS, SS),
and the interface levels of Logical Form (LF) and
Phonetic Form (PF). In this theory, constraints are
highly modularized and apply to syntactic structures
from a certain level of representation onwards.
X-bar is a powerful and compact module of
Principles and Parameters Theory (Adger, 2003;
Carnie, 2013; Sportiche, Koopman and Stabler, 2014)
for the representation of syntactic category formation
in natural language, as it yields hierarchical structures
in binary trees that encode the constituency of a
sentence. The syntactic category or part-of-speech of
a lexical item in a sentence is determined according
to the item’s morphology, grammatical features and
syntactic distribution. Syntactic categories with
referential meaning or content are classified as lexical
(nouns, verbs, adjective, adverbs, prepositions), while
those that strictly serve grammatical purposes and are
required for well-formedness are called functional
(determiners, complementisers, coordinators, tense,
auxiliaries, negation). Heads are lexical items from
which full phrases are formed and they project
themselves into different levels. X-bar theory (where
the variable X stands for a syntactic category)
assumes three syntactic projection levels: minimal,
intermediate and maximal. In the X-bar binary tree
structure, minimal projections or heads (denoted
sometimes as X°) are nuclear categories and do not
dominate any other category, in other words, the
terminal nodes of a syntactic tree. Intermediate
projections (denoted as X' and read as “X-bar”) are
typically generated from the merge of a minimal
projection and a subcategorized complement.
Maximal projections or phrases (denoted as XP) are
the highest level of a nuclear category which has
satisfied all its subcategorization requirements and
may dominate another phrase-level constituent (a
specifier) merged with the intermediate projection.
The X-bar module has only three general rules that
apply to all lexical categories, i.e. the specifier rule,
the complement rule, and the adjunct rule. The
context-free X-bar rules may be combined in any
order so it allows the production of different
structures from the same array of words or lexical
items. As a recursive rule, Adjunction is the most
Towards a Principled Computational System of Syntactic Ambiguity Detection and Representation
unconstrained syntactic operation and is related to
most instances of structural ambiguity. X-bar by
itself is overgenerative, which is problematic for a
descriptively adequate model of linguistic
competence. For this reason, other syntactic modules,
like the Thematic Criterion, Case Filter, Binding
Principles and Bounding Theory, among others, are
needed to impose conditions on the legal
combinations in a language.
More recent proposals call for a Minimalist
Program (Chomsky, 1995, 2000) in the revision of
linguistic theory. Under closer analysis, X-bar may
not be a primitive, independently motivated principle
of Universal Grammar, but the result of Merge, a
more fundamental, recursive, binary operation on
syntactic structures. Also, LF and PF may be the only
required levels of syntactic representation as they are
the interface between the computational system (the
grammar) and both the conceptual-intentional and
sensory-motor external systems.
As a computational model of syntactic analysis, the
system we describe has two main components, i.e.,
the parser and the lexicon. The parsing process
requires an input in the form of a word sequence. The
input sequence is tokenized during the pre-processing
stage. Tokenization involves the segmentation of a
string into lexical items or word elements called
tokens. The syntactic category or part-of-speech
tagging process takes place right after tokenization.
This process will access each tokenized element’s
grammatical features. For the tagging process to be
successful, the tokenized element must be found in
the lexicon. The parser will take the tagged sequence
and will establish a relationship between each tagged
element by generating the syntactic structures of the
input word sequence in the form of binary trees that
correspond to the SS level of syntactic representation.
3.1 The Parser
Parsing can be performed by means of bottom-up or
top-down approaches. In the system described, a
bottom-up, bidirectional algorithm is implemented
that produces multiple binary trees when a sequence
has syntactic ambiguity. The algorithm starts by
segmenting the sequence into tokens that correspond
to lexical items; this is followed by the categorization
of each lexical item and the application of the
syntactic rules in X-bar to produce the parse tree. The
absolute upper bound of structural representations for
a given sequence of n lexical items corresponds to the
n-1 Catalan number, a sequence of natural numbers
that are used in combinatorics to determine, among
other things, the number of distinct binary trees that
can be created from a number of nodes.
Figure 1: Interaction of direction and inspection.
The parser currently takes into account three
parameters in order to be able to produce multiple
representations in case of structural ambiguity. These
parameters are direction, inspection and delay. The
direction parameter determines if the input word
sequence is to be analysed from left to right or from
right to left. The implemented algorithm is
bidirectional (LR and RR) because it analyses word
sequences from left to right and from right to left,
producing in both cases a rightmost derivation in
reverse (Grune and Jacobs, 2008). The inspection
parameter determines if the current lexical element
looks forwards or backwards to compare itself with
another lexical item to check for selection features. If
the analysis is from left to right, the inspection
parameter must be forwards (look one token ahead);
if the analysis is from right to left then the inspection
must be backwards (look one token back).
As it can be seen in (1), the setting of the
inspection parameter is dependent on the direction
parameter. Syntactic heads are categories with
c(ategorial)-selection features or requirements
NLPinAI 2019 - Special Session on Natural Language Processing in Artificial Intelligence
(subcategorization). In the case of head-first
languages as English, the c-selection features of a
head can be checked by a constituent to its right. In
the case of subject-object-verb (SOV) or head-last
languages, the left to right analysis would have
backward inspection (1bi) and the right to left
analysis would have forward inspection (1bii). Since
this parser currently deals with declarative sentences
in English, which show the subject-verb-object
(SVO) constituent order typical of head-first
languages, the inspection parameter is set to forward
if the direction is left to right (1ai), but it is set to
backwards if the direction is right to left (1aii).
Figure 2: Interaction of direction, inspection, and delay.
The delay parameter, which is either true or false,
determines if the formation of determiner phrases
(DPs) not governed by a preposition are delayed until
other phrase formations take place. The delay in the
formation of these DPs, i.e., the merge of a determiner
(D) with a noun phrase (NP), allows the formation of
more complex DPs in certain contexts, particularly
within verbal phrases (VPs). For example,
prepositional phrases (PPs) can sometimes be parsed
as adjuncts (modifiers) of VPs or as adjuncts of NPs,
but not as adjuncts of DPs (for syntactic and semantic
reasons out of the scope of this paper). While an NP
is not merged with a D, the NP may adjoin a PP;
otherwise, the PP can only be merged to the structure
via VP adjunction. For example, the sequence saw a
man with a telescope has two structural descriptions
as shown in (2). Without DP delay (2a), the DPs a
man and a telescope are formed within the same
iteration. In the next iteration, the VP saw a man and
the PP with a telescope are formed; thus, the PP can
only adjoin the VP. However, with DP delay (2b), the
merge of the D a with an NP is delayed, allowing the
formation of a more complex NP with PP adjunction.
As can be observed, the formation of the DP a
telescope is not delayed as this DP is governed by the
P with. Since the direction and inspection parameters
seem to be mutually dependent, the combination of
these three parameters will produce a total of four
variations of the same basic algorithm. This allows
for the possible production of up to four syntactic
representations for structurally ambiguous word
3.2 The Lexicon
The lexicon is the language module that contains the
grammatical information about the lexical items in
the sentence that is to be analysed by the parser. Since
it is necessary to determine if a certain combination
of words is licensed or grammatical in the language,
this system also requires the construction of a robust
lexicon that at least contains the syntactic category,
subcategorization frames and relevant grammatical
features (such as case, c-selectional and phi- [or
agreement] features) for each lexical item. As Fong,
(2005:313) defines it, the lexicon is “the heart of the
implemented system.”
For this system, the lexicon was manually tagged
by a team of linguists. To facilitate pre-processing,
the lexicon contains every fully inflected word-form
appearing in a corpus of 200 sentences that were
constructed for validation purposes. Lexical items
are entered as a string of literals, and features are
indicated by means of different data types. All lexical
items are labelled with a syntactic category;
additionally, each category requires a specific subset
of valued features.
Verb. The main predicative category, verbs are
labelled according to their argument structure with
the subclasses transitive, ditransitive and intransitive,
and by their tense, aspect and mood (TAM) features
as ±pret, perf, prog, pas, base. Transitive and
ditransitive verbs are assigned a case feature,
acc(usative), and are given a subcategorization frame
for arg1; ditransitive verbs subcategorize also for
Towards a Principled Computational System of Syntactic Ambiguity Detection and Representation
arg2. Optional c-selection features in
subcategorization frames are indicated within
parentheses. Intransitive verbs, on the other hand,
which include both unergative and unaccusative
verbs, are not assigned case or c-selection features.
As for TAM features, verb forms tagged as ±pret,
perf, prog, and pas are also labelled as finite with a 1
bit, and those tagged as base were labelled as non-
finite with a 0 bit. In the case of passive participles,
they are tagged as pas, no case feature is assigned,
their arg1 frame is replaced with their arg2 frame,
leaving the arg2 frame subsequently as an empty list.
In this way, the parser could be able to analyse
passive declarative clauses without accounting for
syntactic movement or thematic roles.
Auxiliary. Items of this category are tagged with a
subclass: Perf, Prog, Pas. Each auxiliary c-selects for
a specific subclass of AuxP or VP with a particular
TAM feature. For example, the perfect auxiliary has
c-selects a VP (or another AuxP) with TAM perf: He
has interrogated the witness; He has been
T(ense). This category includes true modals and
tense/finiteness markers. Items of this category
always precede the negation particle not, are assigned
a nom(inative) case feature, and c-select either a VP,
an AuxP or a NegP as arg1 and (with the exception of
infinitival to) a DP or CP as arg0. As a consequence,
T will always merge with a verbal (functional)
projection as complement and with a nominal or a
clause-level projection in their specifier as a subject.
Thus, the universal requirement that every clause
must have a subject (known in the syntactic literature
as the Extended Projection Principle or EPP) is
satisfied to check the c-selection features in arg0.
Complementiser. With the exception of small
clauses and raising structures, complementisers (Cs)
are the maximal functional category of clauses and
sentences. At this stage of the implemented system,
complementisers are only labelled by their force as
±Q, although the parser is not yet handling
interrogatives. Cs c-select a TP complement, and it is
so specified in the arg1 frame.
Noun. Words of this category are classified as
common or proper, which is syntactically relevant as
the latter subclass does not generally admit
determiners (at least definite and indefinite articles)
in English. Nouns can also have argument structure,
specially deverbal nouns, which they inherit from the
verb they are derived from. But, unlike verbs, the c-
selection features of nouns need not be checked in
order to produce a well-formed structure: The
destruction was imminent; the destruction of
Carthage was imminent.
Determiner. This nominal functional category
includes articles, demonstratives, possessives, as well
as quantifiers and personal pronouns. The distribution
of DPs is highly constrained: all (referential) DPs
must be syntactically licensed by certain heads in
order to appear in well-formed structures. Thus, the
syntactic module of Case Theory rules the licensing
and distribution of DPs. In order to implement this
constraint, all Ds have to check a formal case feature
with a licensing head: finite T, which checks (nom);
transitive V, (acc); prepositions (Ps), obl(ique). Most
determiners (with the exception of personal
pronouns) may check any case in order to be licensed.
On the other hand, since case in personal pronouns is
morphological and not abstract, they must check a
specific case according to their morphology: he must
check nom; him must check either acc or obl. Non-
pronominal determiners also c-select a complement
NP, which is specified in the arg1 frame.
Other categories, such as adjective, adverb and
preposition, are also labelled in the lexicon with the
applicable features.
The system was implemented in Python 3.6.1. The
lexicon was provided and stored in a MySQL
database system. The tagging process needed the
pymysql external library in order to communicate
with the database. The parsing algorithm was purely
implemented in python but the representation of the
binary trees was represented through the use of the
external libraries Plotly and igraph.
As for the database system, a decision was made
between different technologies that best fitted the
system. MySQL was chosen mainly because of its
compatibility with all operating systems and
horizontal partitioning. As soon as the lexicon was
completed, the design of the database started by
defining its entity relationship diagram, how the
tables should look, and the queries to be utilized for
the algorithm. The database uses four tables: Lexicon,
Arguments, Case, and Contractions. As each lexical
item has a maximum of three arguments, a separate
table was created to avoid mistakes on the lexicon. It
should also be noted that, since arguments are
represented as a string separated with commas, there
is no intermediate table to assign an argument to each
lexical item, since that is exactly what the tagging
component will be expecting to receive. The final
table, Contractions, was created to manage
NLPinAI 2019 - Special Session on Natural Language Processing in Artificial Intelligence
contractions in a dynamic way. The tokenizer
communicates with the server and, if a contraction is
found on the sentence, it is separated in the two
lexical items that form the contraction so the parser
can analyse them. This Contractions table bears no
relationship with the other tables created so far.
The system has a pipeline structure shown in (3):
Figure 3: Pipeline structure of the system.
The system efficiency was tested with a set of 200
declarative sentences that was constructed to validate
the algorithm. The two major aspects of testing
involved grammaticality judgement and ambiguity
detection. Ambiguous sequences have first to be
considered as grammatical before alternate
representations are produced. For the system to pass
the grammaticality judgement test for a particular
sequence, at least one structural representation must
be produced for the sequence to be identified as
grammatical. On the other hand, to pass the ambiguity
detection test, the analyser had to produce at least two
syntactic representations for a structurally ambiguous
sequence. The parser correctly identified a sequence
as grammatical for 78% of the validation sentences.
The ambiguity detection test resulted in an 82.76%
success rate for the grammatically identified
sequences. However, some structures were
particularly difficult for the parser to judge as
grammatical, like for example, subordinate finite
clauses with null complementisers, as in I think he
will not leave the house. Although grammatically
correct, this sequence failed to parse due to the fact
that the verb think assigns accusative case to
determiners and pronouns, yet the case feature of he
is nominative, which was interpreted by the parser as
a case feature mismatch. As expected, the sequence is
judged correctly when the complementiser is overt (I
think that he will not leave the house). Non-finite
clauses were also challenging, like infinitival subjects
(To err is human), which the current algorithm does
not parse, and adjunct gerundive clauses (I will meet
John eating waffles), since the parser expected
present participles to be licensed by a progressive
auxiliary. Double object constructions (Poirot
promised Maigret the job last week) and Saxon
genitive constructions (The army's destruction of the
city was imminent) were typically misjudged by the
parser as ungrammatical, on account of a case
checking failure. Finally, for some sequences,
although correctly judged as grammatical, the parser
did not detect ambiguity (I ate the macaroni that my
mother cooked yesterday).
The validation test results were promising, yet there
are still some foreseen limitations in the system.
Currently, the parser cannot produce all possible
adjunctions in cases of four or more consecutive
maximum projections on which adjunction can be
performed (such as APs, PPs, and CPs for NPs;
AdvPs, PPs and CPs for VPs). Two mechanisms have
been identified to overcome this limitation. First, a
new parameter, adjunction implementation, may be
added to the algorithm. This parameter may signal
either of two methods: sequential adjunction, in
which only the original nodes of the current iteration
would be available objects for adjunction, or nested
adjunction, in which the newly formed nodes by
previous adjunction within an iteration would be
available as well for this operation. Each method
produces distinct results in sequences of four or more
consecutive maximal projections that can be
adjoined. A second, perhaps simpler and more elegant
mechanism, involves a recursive method that
generates all possible adjunction patterns of this
binary operation.
Due to time constraints in the project
development, the current implementation deals with
CPs in a somewhat different way than other phrases,
by using a top-down rule instead of the typical
bottom-up strategy used everywhere else. This fact
may be the cause of some failures in infinitival
subject analysis and other non-finite clauses, as well
as occasional flaws in ambiguity detection in
subordinate clauses. This present limitation is
expected to be relatively easy to overcome by adding
extra rules or functionalities to the implementation.
Noun phrases allow for empty determiners (nude
NPs) for certain kinds of noun heads. Not only the
distinction between common and proper nouns is
relevant in this regard, but also the distinction
between count and mass nouns is necessary for the
correct grammaticality judgement of nude NPs in
sentences. Some polysemic nouns have a meaning
associated with the mass noun class and another with
the count noun class (as with resistance). This
requires additional tagging in the lexicon for the
Towards a Principled Computational System of Syntactic Ambiguity Detection and Representation
distinction between these two subclasses, along with
a method in the parsing implementation to handle the
distinction appropriately. Also within the nominal
domain, the Saxon genitive construction was
challenging. A solution to the problem requires a
treatment similar to the contractions and labelling ’s
as a D that c-selects an arg0.
Double object constructions, or even ditransitives
in general, may present a challenge to our current
system. For similar reason, complex NPs with more
than one argument may not be correctly parsed.
Among possible solutions to this problem is the
inclusion of other functional or light categories to
allow for richer structural representations.
Currently, all intransitive verbs, either
unaccusatives or unergatives, are handled in the same
way by the system. This may be problematic for the
analysis of passive construction or for auxiliary
selection in languages where the choice of certain
verbal auxiliaries is dependent on whether the verb is
unaccusative or not. Again, a richer VP-internal
structure representation may be needed, as well as
some implementation of a Theta-Theory module for
both the parser and the lexicon.
Since the current parser does not account for
syntactic movement, structures that require overt
transformations such as interrogatives and relative
clauses are not analysed. A single syntactic object
may not comply with all required conditions, but a
chain structure consisting of a moved object (such as
a DP) and its trace in its base position would comply
simultaneously with, for example, Case Filter and
Theta Theory, respectively. Different mechanisms
are being considered for its implementation.
Along with the inclusion of mechanisms to
account for movement, it would be necessary for the
parser to recognize locality of dependencies and
violations thereof, for which a Bounding Theory
module must be implemented.
Referential ambiguity is not detected by the
present system, as it requires additional data
structures, as indices for coreferentiality and the
implementation of Binding Conditions on the
interpretation of referential expressions and
pronouns. Various mechanisms should be considered
to overcome this limitation.
The structural representations generated by this
system mostly correspond to the SS level of syntactic
representation. At this level, certain operator scope
ambiguities may not be detected. These scope
differences are encoded in the LF interface level,
where it is argued that operators such as quantifiers or
interrogative expressions covertly move, obeying the
same movement restrictions and locality conditions
of overt movements. To achieve this, the system
would have to generate LF structural representations
instead. Operating on logical forms would also
facilitate the integration of this system with semantic
functionalities such as semantic composition and
valuation, and textual entailment.
Fortunately, this computational system has been
purposely designed to have a scalable software
framework, so that more functionalities may be added
with minimum impact on current methods and data
structures. Although at this stage the system has been
implemented exclusively to analyse English
sentences, it may well suit typologically-diverse
natural languages. The lexicon database may be
easily augmented and the language-specific methods
to handle English sequences are minimal, an
advantage inherent to principled-based over rule-
based systems.
This project was born from the fruitful discussions
within the CompLing/NLP Research Group at the
University of Puerto Rico-Mayagüez (UPRM) The
authors would like to acknowledge the contributions
of the other members of Team Forest at UPRM:
Orlando Alverio, who was responsible for the system
database and user interface, and the Linguistics team
who were in charge of lexical tagging: Maday
Cartagena, Jerry Cruz, Luis Irizarry, Joshua Mercado,
Alejandra Santiago, and Ammerys Vázquez. David
Riquelme and Victor Lugo reviewed the parser’s
output and helped with the validation process. The
authors are also indebted to two anonymous
reviewers for their valuable observations and
Adger, D., 2003. Core Syntax: A Minimalist Approach,
Oxford University Press. Oxford.
Berwick, R.,1985. The acquisition of syntactic knowledge,
MIT Press. Cambridge, Mass.
Carnie, A., 2013. Syntax: A Generative Introduction,
Blackwell. Oxford, 3
Chesi, C., 2004. Phases and Cartography in Linguistic
Computation. Ph.D. Thesis. University of Siena.
Chesi, C., 2012. Competence and Computation: Towards a
Processing Friendly Minimalist Grammar, Unipress.
Chomsky, N., 1981. Lectures on Government and Binding,
Mouton de Gruyter. Berlin.
NLPinAI 2019 - Special Session on Natural Language Processing in Artificial Intelligence
Chomsky, N., 1995. The Minimalist Program, MIT Press.
Cambridge, Mass.
Chomsky, N., 2000. Minimalist Inquiries: The Framework.
In Martin, R. et al., 2000. Step by Step. Essays on
Minimalist Syntax in Honor of Howard Lasnik.
Cambridge, Mass: MIT Press.
Collins, C., Stabler, E., 2016. A Formalization of
Minimalist Syntax. Syntax 19,1.
Fong, S., 1991. Computational Properties of Principle-
Based Grammatical Theories. Ph.D. Thesis. MIT.
Fong, S., 2004. Computation with probes and goals. In Di
Sciullo, 2004. Amsterdam: John Benjamins.
Gomez-Marco, O., 2015. Towards an X-Bar parser: A
model of English syntactic performance. M.S. Thesis.
University of Puerto Rico-Mayagüez.
Grune, J., Jacobs, C.J.H., 2008. Parsing Techniques. A
Practical Guide, Springer. Amsterdam, 2
Joshi, A., Schabes, Y., 1997. Tree-Adjoining Grammars. In
Rozenberg, G. and Salomaa, A., 1997. Berlin: Springer.
Partee, B., 2004. Compositionality in Formal Semantics,
Blackwell. Oxford.
Partee, B., ter Meulen, A., Wall, R., 1990. Mathematical
methods in linguistics, Kluwer Academic. Dordrecht.
Phillips, C., 1996. Order and Structure. Ph.D. Thesis. MIT.
Sportiche, D., Koopman, H., Stabler, E., 2013. An
introduction to syntactic analysis and theory, John
Wiley and Sons. Oxford.
Stabler, E., 1997. Derivational Minimalism. In Retoré, C.,
1997. Logical Aspects of Computational Linguistics.
Berlin: Springer.
Stabler, E., 2011. Computational Perspectives on
Minimalism. In Boeckx, C., 2011. The Oxford
Handbook of Linguistic Minimalism. Oxford: OUP.
Towards a Principled Computational System of Syntactic Ambiguity Detection and Representation