Separating Dependency from Constituency in a Tree Rewriting System

In this paper we present a new tree-rewriting formalism called Link-Sharing Tree Adjoining Grammar (LSTAG) which is a variant of synchronous TAGs. Using LSTAG we define an approach towards coordination where linguistic dependency is distinguished from the notion of constituency. Such an approach towards coordination that explicitly distinguishes dependencies from constituency gives a better formal understanding of its representation when compared to previous approaches that use tree-rewriting systems which conflate the two issues.Comment: 7 pages, 6 Postscript figures, uses fullname.st

Hybrid grammars for parsing of discontinuous phrase structures and non-projective dependency structures

We explore the concept of hybrid grammars, which formalize and generalize a range of existing frameworks for dealing with discontinuous syntactic structures. Covered are both discontinuous phrase structures and non-projective dependency structures. Technically, hybrid grammars are related to synchronous grammars, where one grammar component generates linear structures and another generates hierarchical structures. By coupling lexical elements of both components together, discontinuous structures result. Several types of hybrid grammars are characterized. We also discuss grammar induction from treebanks. The main advantage over existing frameworks is the ability of hybrid grammars to separate discontinuity of the desired structures from time complexity of parsing. This permits exploration of a large variety of parsing algorithms for discontinuous structures, with different properties. This is confirmed by the reported experimental results, which show a wide variety of running time, accuracy and frequency of parse failures.Publisher PDFPeer reviewe

Olga Mišeska Tomic, Syntax and Syntaxes: The Generative Approach to English Sentence Analysis; Savremena administracija, Beograd, 1987

Olga Mišeska Tomic,  professor of  English Linguistics at the  Universities of Skopje nad Novi Sad, has written a well-organised survey of major syntactical issues in the generative approach in general and as it applies to English sentence grammar. It is a well-researched book with and astonishing amount  of information, readily comprehensible to the language student,  for whom it is principally aimed. The au­ thor skilfully conducts the reader through quite complex matters, carefully avoiding confusion between simplification and distortion. The main aim of the book, as out­ lined in the brief Preface, is to expose the assumptions and problems involved in the conceptualisation of the issues rather than the technical, theory-internal  formaliza­ tions. This is by no means a simple task and for most of the ground covered the auth or has had to construct the emerging picture from a patchwork of specialized con­ tributions  to individual aspects of English sentence construction

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.Comment: Accepted in ACM Transactions on Information System

Algebraic dependency grammar

We propose a mathematical formalism called Algebraic Dependency Grammar with applications to formal linguistics and to formal language theory. Regarding formal linguistics we aim to address the problem of grammaticality with special attention to cross-linguistic cases. In the field of formal language theory this formalism provides a new perspective allowing an algebraic classification of languages. Notably our approach suggests the existence of so-called anti-classes of languages associated to certain classes of languages. Our notion of a dependency grammar is as of a definition of a set of well-constructed dependency trees (we call this algebraic governance) and a relation which associates word-orders to dependency trees (we call this algebraic linearization). In relation to algebraic governance, we define a manifold which is a set of dependency trees satisfying an agreement condition throughout a pattern, which is the algebraic form of a collection of syntactic addresses over the dependency tree. A boolean condition on the words formalizes the notion of agreement. In relation to algebraic linearization, first we observe that the notion of projectivity is quintessentially that certain substructures of a dependency tree always form an interval in its linearization. So we have to establish well what is a substructure; we see again that patterns proportion the key, generalizing the notion of projectivity with recursive linearization procedures. Combining the above modules we have the formalism: an algebraic dependency grammar is a manifold together with a linearization. Notice that patterns sustain both manifolds and linearizations. We study their interrelation in terms of a new algebraic classification of classes of languages. We highlight the main contributions of the thesis. Regarding mathematical linguistics, algebraic dependency grammar considers trees and word-order different modules in the architecture, which allows description of languages with varied word-order. Ellipses are permitted; this issue is usually avoided because it makes some formalisms non-decidable. We differentiate linguistic phenomena structurally by their algebraic description. Algebraic dependency grammar permits observance of affinity between linguistic constructions which seem superficially different. Regarding formal language theory, a new system for understanding a very large family of languages is presented which permits observation of languages in broader contexts. We identify a new class named anti-context-free languages containing constructions structurally symmetric to context-free languages. Informally we could say that context-free languages are well-parenthesized, while anti-context-free languages are cross-serial-parenthesized. For example copy languages and respectively languages are anti-context-free.Es proposa un formalisme matemàtic anomenat Gramàtica de Dependències Algebraica amb aplicacions a la lingüística formal i a la teoria de llenguatges formals. Pel que fa a la lingüística formal es pretén abordar el problema de la gramaticalitat, amb un èmfasi especial en la transversalitat, això és, que el formalisme sigui apte per a un bon nombre de llengües. En el camp dels llenguatges formals aquest formalisme proporciona una nova perspectiva que permet una classificació algebraica dels llenguatges. Aquest enfocament suggereix a més a més l'existència de les aquí anomenades anti-classes de llenguatges associades a certes classes de llenguatges. La nostra idea d'una gramàtica de dependències és en un conjunt de sintagmes ben construïts (d'això en diem recció algebraica) i una relació que associa ordres de paraules als sintagmes d'aquest conjunt (d'això en diem linearització algebraica). Pel que fa a la recció algebraica, introduïm el concepte de varietat sintàctica com el conjunt de sintagmes que satisfan una concordança sobre un determinat patró. Un patró és un conjunt d'adreces sintàctiques descrit algebraicament. La concordança es formalitza a través d'una condició booleana sobre el vocabulari. En relació amb linearització algebraica, en primer lloc, observem que l'essencial de la noció clàssica de projectivitat rau en el fet que certes subestructures d'un arbre de dependències formen sempre un interval en la seva linearització. Així doncs, primer hem d'establir bé que vol dir subestructura. Un cop més veiem que els patrons en proporcionen la clau, tot generalitzant la noció de projectivitat a través d'un procediment recursiu de linearització. Tot unint els dos mòduls anteriors ja tenim el nostre formalisme a punt: una gramàtica de dependències algebraica és una varietat sintàctica juntament amb una linearització. Notem que els patrons són a la base de tots dos mòduls: varietats i linearitzacions, així que resulta del tot natural estudiar-ne la interrelació en termes d'un nou sistema de classificació algebraica de classes de llenguatges. Destaquem les principals contribucions d'aquesta tesi. Pel que fa a la matemàtica lingüística, la gramàtica de dependències algebraica considera els arbres i l'ordre de les paraules diferents mòduls dins l'arquitectura la qual cosa permet de descriure llenguatges amb una gran varietat d'ordre. L'ús d'el·lipsis és permès; aquesta qüestió és normalment evitada en altres formalismes per tal com la possibilitat d'el·lipsis fa que els models es tornin no decidibles. El nostre model també ens permet classificar estructuralment fenòmens lingüístics segons la seva descripció algebraica, així com de copsar afinitats entre construccions que semblen superficialment diferents. Pel que fa a la teoria dels llenguatges formals, presentem un nou sistema de classificació que ens permet d'entendre els llenguatges en un context més ampli. Identifiquem una nova classe que anomenem llenguatges anti-lliures-de-context que conté construccions estructuralment simètriques als llenguatges lliures de context. Informalment podríem dir que els llenguatges lliures de context estan ben parentetitzats, mentre que els anti-lliures-de-context estan parentetitzats segons dependències creuades en sèrie. En són mostres d'aquesta classe els llenguatges còpia i els llenguatges respectivament.Postprint (published version

Discontinuous Constituency and BERT: A Case Study of Dutch

In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch. We devise a test suite based on a mildly context-sensitive formalism, from which we derive grammars that capture the linguistic phenomena of control verb nesting and verb raising. The grammars, paired with a small lexicon, provide us with a large collection of naturalistic utterances, annotated with verb-subject pairings, that serve as the evaluation test bed for an attention-based span selection probe. Our results, backed by extensive analysis, suggest that the models investigated fail in the implicit acquisition of the dependencies examined

Query-Driven Global Graph Attention Model for Visual Parsing: Recognizing Handwritten and Typeset Math Formulas

We present a new visual parsing method based on standard Convolutional Neural Networks (CNNs) for handwritten and typeset mathematical formulas. The Query-Driven Global Graph Attention (QD-GGA) parser employs multi-task learning, using a single feature representation for locating, classifying, and relating symbols. QD-GGA parses formulas by first constructing a Line-Of-Sight (LOS) graph over the input primitives (e.g handwritten strokes or connected components in images). Second, class distributions for LOS nodes and edges are obtained using query-specific feature filters (i.e., attention) in a single feed-forward pass. This allows end-to-end structure learning using a joint loss over primitive node and edge class distributions. Finally, a Maximum Spanning Tree (MST) is extracted from the weighted graph using Edmonds\u27 Arborescence Algorithm. The model may be run recurrently over the input graph, updating attention to focus on symbols detected in the previous iteration. QD-GGA does not require additional grammar rules and the language model is learned from the sets of symbols/relationships and the statistics over them in the training set. We benchmark our system against both handwritten and typeset state-of-the-art math recognition systems. Our preliminary results show that this is a promising new approach for visual parsing of math formulas. Using recurrent execution, symbol detection is near perfect for both handwritten and typeset formulas: we obtain a symbol f-measure of over 99.4% for both the CROHME (handwritten) and INFTYMCCDB-2 (typeset formula image) datasets. Our method is also much faster in both training and execution than state-of-the-art RNN-based formula parsers. The unlabeled structure detection of QDGGA is competitive with encoder-decoder models, but QD-GGA symbol and relationship classification is weaker. We believe this may be addressed through increased use of spatial features and global context