Tree Adjoining Grammars Research Articles

A Survey on Syntactic Pattern Recognition Methods in Bioinformatics

Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.

Lexical Functional Grammar as a Construction Grammar

Lexical Functional Grammar (LFG) is a lexicalist, constraint-based grammatical theory that shares a lot of the basic assumptions of Construction Grammar (CxG), such as a commitment to surface-oriented descriptions (no transformations), and the simultaneous representation of form, meaning, and other grammatical information (no derivations). Nevertheless, LFG is not standardly viewed as a kind of CxG, in particular since its adherence to the principle of Lexical Integrity means that it insists on a strict morphology-syntax distinction where CxG canonically rejects such a divide. However, such a distinction is in fact entirely compatible with CxG assumptions; the actual problem with viewing LFG as a CxG is the difficulty it has in describing the more substantive end of the schematic-substantive spectrum of constructions. I suggest that by replacing the limited context-free grammar base of LFG responsible for this shortcoming with a more expressive formalism (in this case a description-based tree-adjoining grammar), we can obtain a fully constructional LFG, suitable as a formal framework for CxG.

Generating Arabic TAG for syntax-semantics analysis

AbstractArabic presents many challenges for automatic processing. Although several research studies have addressed some issues, electronic resources for processing Arabic remain relatively rare or not widely available. In this paper, we propose a Tree-adjoining grammar with a syntax-semantic interface. It is applied to the modern standard Arabic, but it can be easily adapted to other languages. This grammar named “ArabTAG V2.0” (Arabic Tree Adjoining Grammar) is semi-automatically generated by means of an abstract representation called meta-grammar. To ensure its development, ArabTAG V2.0 benefits from a grammar testing environment that uses a corpus of phenomena. Further experiments were performed to check the coverage of this grammar as well as the syntax-semantic analysis. The results showed that ArabTAG V2.0 can cover the majority of syntactical structures and different linguistic phenomena with a precision rate of 88.76%. Moreover, we were able to semantically analyze sentences and build their semantic representations with a precision rate of about 95.63%.

Lexicalised Locality: Local Domains and Non-Local Dependencies in a Lexicalised Tree Adjoining Grammar

Contemporary generative grammar assumes that syntactic structure is best described in terms of sets, and that locality conditions, as well as cross-linguistic variation, is determined at the level of designated functional heads. Syntactic operations (merge, MERGE, etc.) build a structure by deriving sets from lexical atoms and recursively (and monotonically) yielding sets of sets. Additional restrictions over the format of structural descriptions limit the number of elements involved in each operation to two at each derivational step, a head and a non-head. In this paper, we will explore an alternative direction for minimalist inquiry based on previous work, e.g., Frank (2002, 2006), albeit under novel assumptions. We propose a view of syntactic structure as a specification of relations in graphs, which correspond to the extended projection of lexical heads; these are elementary trees in Tree Adjoining Grammars. We present empirical motivation for a lexicalised approach to structure building, where the units of the grammar are elementary trees. Our proposal will be based on cross-linguistic evidence; we will consider the structure of elementary trees in Spanish, English and German. We will also explore the consequences of assuming that nodes in elementary trees are addresses for purposes of tree composition operations, substitution and adjunction.

Strong Equivalence of TAG and CCG

Tree-adjoining grammar (TAG) and combinatory categorial grammar (CCG) are two well-established mildly context-sensitive grammar formalisms that are known to have the same expressive power on strings (i.e., generate the same class of string languages). It is demonstrated that their expressive power on trees also essentially coincides. In fact, CCG without lexicon entries for the empty string and only first-order rules of degree at most 2 are sufficient for its full expressive power.

An Initial Study of Tree-Adjoining Grammar Formalism for Parsing Manipuri Language

We present an initial study into the representation of tree-adjoining grammar formalism for parsing Manipuri language. Being a low resource and computationally less researched language, it is difficult to achieve a natural language parser for Manipuri. Treebanks, which are the main requirement for inducing data-driven parsers, are not available for Manipuri. In this paper, we present an extensive analysis of the Manipuri language structure and formulate a lexicalized tree-adjoining grammar. A generalized structure of Manipuri phrases, clauses and the structure of basic and derived sentences have been presented. The sentence types covered in our analysis are that of simple, compound and complex sentences. Using the tree-adjoining grammar we have formulated, one can implement a Manipuri parser whose results can be of immense help in creating a Treebank for Manipuri.

Exploring a genotype based formalisation for tree adjoining grammar derivations

Tree Adjoining Grammars (TAGs) are very useful psycholinguistic formalisms for syntax and dependency analysis of phrase structures. Since natural languages are finitely ambiguous, TAGs are ideal to model them being mildly context sensitive. But these grammars are very hard to parse as they have a worst case complexity of $$O(n^6)$$ . In reality most conventional TAG parsing algorithms run in $$O(n^3)$$ and give the most probable parse, but trying to extract multiple ambiguous parses for a given phrase degrades the performance of the traditional TAG parsing algorithms toward worst case runtime, especially for longer sentences. Ambiguity in TAGs are not as well understood as in CFGs due to their complex derivation process; hence one of the ways to understand it is to find a finite set of ambiguous derivations for the given phrase structure. In this article we extend the definition of the containing formalism introduced as GATAGs on which a genetic algorithm can be deployed in order to find ambiguous derivation structures for longer sentences. We shall formalise the genotypes, phenotypes and the fitness functions for the entailing genetic algorithm, exploring various avenues for their efficient computations. Our main objective here is to explore the possibility of random derivations evolving to good derivations though a natural selection.

A combined syntactic-semantic embedding model based on lexicalized tree-adjoining grammar

This paper presents a joint syntactic-semantic embedding model which not only uses syntactic information to enrich the word embeddings but also generates distributed representations for the syntactic structures themselves. The syntactic input to our model comes from a Lexicalized Tree-Adjoining Grammar parser. The word embeddings from our model outperform the Skip-gram embeddings in several word similarity and sentiment classification experiments. The syntactic structure embeddings help improve a transition-based dependency parser by a clear margin.

Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

Abstract Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

Data-driven System Identification of Thermal Systems using Machine Learning

Abstract The paper addresses the identification of spatial-temporal mirror surface deformations as a result of laser-based heat load within the lithography process of integrated circuit production. The thermal diffusion and surface deformation are modeled by separation of the spatial-temporal effects using data-driven orthogonal decomposition. A novel tree adjoining grammar (TAG) and sparsity enhanced symbolic-regression-based learning methods are deployed to discover temporal dynamics that connect the spatial variation. The resulting data-driven procedure is applied to automatically synthetise a compact model representation of synthetic thermal effects induced mirror surface deformations.

Learnability in the Context of Lexicalized Parameters: A Compositional Approach to Language Acquisition

Language acquisition is inextricably linked to the structure of language itself. Consequently, syntactic acquisition must address the structure of syntactic representation. Research on cross-linguistic syntactic variation and acquisition has repeatedly demonstrated the various difficulties in modeling a Principles and Parameters-style grammar and learning mechanism. Perhaps, such difficulty stems from the inadequacy of a Principles and Parameters-style representation of language itself. This paper combines relevant aspects of Minimalist syntax and a learning-by-parsing approach, in the vein of Fodor and Teller (2000), in order to motivate a reanalysis of learnability using lexicalized parameter values. The learner’s goal is no longer to set grammar-wide parameter values, but rather to learn how individual lexical items are merged. In this paper, a new psychologically-plausible learnability model is mapped as the foundation for future research. Furthermore, by abstracting away from mental primitives, this paper also notes how tree-adjoining grammars may be used as computational proxies for lexicalized parameters, providing empirical ways to further study this proposed learning algorithm.

A Tree Adjoining Grammar representation for models of stochastic dynamical systems

Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box–Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.

Using syntax for improving phrase-based SMT in low-resource languages

AbstractData driven approaches for machine translation, such as statistical and neural machine translation, suffer from sparsity when dealing with low-resource languages. In these cases, using other sources of information including linguistic information could alleviate the problem. In this article, we focus on the problem of word ordering in translation from a high-resource to a low-resource language and try to improve the quality by using syntactic information from the high-resource side. We propose some syntactic features based on Tree Adjoining Grammar (TAG) to be employed in a phrase-based SMT model in order to improve the word ordering. In this work, a set of synchronous TAG rules is extracted and used to estimate the probability of the phrase orders suggested by the phrase-based model. The main idea of the article is to handle the word ordering by using the extended domain of locality property of TAG and abstracting the long distance dependencies into a local view, which is a TAG elementary tree. The experiments on English–Persian and English–German translation showed that, by combining the proposed TAG-based reordering features with lexical and hierarchical reordering models, we gain significant improvements over the baseline and in comparison with a neural reordering model and a pre-reordering model.

On trans-derivational operations: generative semantics and tree adjoining grammar

The possibility of formulating conditions that made reference to multiple derivations was a staple of early work in Generative Semantics. In this paper we explore the possibility of having constraints arise from the interaction between derivations in an extended Generative Semantics-type (GS) grammar. In GS syntactic operations apply to semantic material in phrase structure trees; in our extension, these operations can apply within and across derivations. To this end, we will appeal to Tree Adjoining Grammars (TAG; Joshi, 1985 and much subsequent work). The model we propose thus assumes two basic operations, which are the singulary and the generalized versions of a single compositional generative operation. The singulary, tree-internal version is Predicate Raising, which ‘adjoins a predicate to the next higher predicate’ (McCawley, 1973a [1968]: 157); the generalized version is Adjoin, which ‘composes an auxiliary tree β with a tree γ’ (Joshi, 1985: 209), thus applying trans-derivationally. The empirical motivation for this expansion of generative semantics comes from the analysis of an apparently anomalous case of secondary predication in Spanish and English, which –we argue- shares properties with depictive and resultative constructions but in fact is neither.

Syntactic composition and selectional preferences in Hindi Light Verb Constructions

Previous work on light verb constructions (e.g. chorii kar ‘theft do; steal’) in Hindi describes their syntactic formation via co-predication (Ahmed et al., 2012, Butt, 2014). This implies that both noun and light verb contribute their arguments, and these overlapping argument structures must be composed in the syntax. In this paper, we present a co-predication analysis using Tree-Adjoining Grammar, which models syntactic composition and semantic selectional preferences without transformations (deletion or argument identification). The analysis has two key components (i) an underspecified category for the nominal and (ii) combinatorial constraints on the noun and light verb to specify selectional preferences. The former has the advantage of syntactic composition without argument identification and the latter prevents over-generalization, while recognizing the semantic contribution of both predicates. This work additionally accounts for the agreement facts for the Hindi LVC.

Multiple context-free tree grammars: Lexicalization and characterization

Multiple (simple) context-free tree grammars are investigated, where “simple” means “linear and nondeleting”. Every multiple context-free tree grammar that is finitely ambiguous can be lexicalized; i.e., it can be transformed into an equivalent one (generating the same tree language) in which each rule of the grammar contains a lexical symbol. Due to this transformation, the rank of the nonterminals increases at most by 1, and the multiplicity (or fan-out) of the grammar increases at most by the maximal rank of the lexical symbols; in particular, the multiplicity does not increase when all lexical symbols have rank 0. Multiple context-free tree grammars have the same tree generating power as multi-component tree adjoining grammars (provided the latter can use a root-marker). Moreover, every multi-component tree adjoining grammar that is finitely ambiguous can be lexicalized. Multiple context-free tree grammars have the same string generating power as multiple context-free (string) grammars and polynomial time parsing algorithms. A tree language can be generated by a multiple context-free tree grammar if and only if it is the image of a regular tree language under a deterministic finite-copying macro tree transducer. Multiple context-free tree grammars can be used as a synchronous translation device.

A syntax-semantics interface for Tree-Adjoining Grammars through Abstract Categorial Grammars

We present a model of the syntax-semantics interface for Tree-Adjoining Grammars (TAGs). It is based on the encoding of TAGs within the framework of Abstract Categorial Grammars (ACGs). This encoding relies on a functional interpretation of the substitution and adjunction operations of TAGs. In ACGs, the abstract terms representing derivation trees are full-fledged objects of the grammar. These terms are mapped onto logical formulas representing the semantic interpretation of natural expressions that TAGs can analyze. Because of the reversibility properties of ACGs, this provides a way to parse and generate with the same TAG encoded grammar. We propose several analyses, including long distance dependencies, quantification, control and raising verbs, and subordinate clauses. We also show how this encoding easily extends to other phenomena such as idioms or scope ambiguities. All the lexical data for theses modellings are provided and can be run with the ACG toolkit, a software package dedicated to the development of ACGs that can use these grammars both for parsing and generation.

Syntactic discontinuities in Latin – A treebank-based study

Syntactic discontinuities are very frequent in classical Latin and yet this data was never considered in debates on how expressive grammar formalisms need to be to capture natural languages. In this paper I show with treebank data that Latin frequently displays syntactic discontinuities that cannot be captured in standard mildly context-sensitive frameworks such as Tree-Adjoining Grammars or Combinatory Categorial Grammars. I then argue that there is no principled bound on Latin discontinuities but that they display a broadly Zipfian distribution where frequency drops quickly for the more complex patterns. Lexical-Functional Grammar can capture these discontinuities in a way that closely reflects their complexity and frequency distributions.

Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication

We describe a recognition algorithm for a subset of binary linear context-free rewriting systems (LCFRS) with running time O(nωd) where M(m) = O(mω) is the running time for m × m matrix multiplication and d is the “contact rank” of the LCFRS—the maximal number of combination and non-combination points that appear in the grammar rules. We also show that this algorithm can be used as a subroutine to obtain a recognition algorithm for general binary LCFRS with running time O(nωd+1). The currently best known ω is smaller than 2.38. Our result provides another proof for the best known result for parsing mildly context-sensitive formalisms such as combinatory categorial grammars, head grammars, linear indexed grammars, and tree-adjoining grammars, which can be parsed in time O(n4.76). It also shows that inversion transduction grammars can be parsed in time O(n5.76). In addition, binary LCFRS subsumes many other formalisms and types of grammars, for some of which we also improve the asymptotic complexity of parsing.

LR Parsing for LCFRS

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LR-style parsing algorithm for Linear Context-Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years in the context of natural language processing.