Graph Interpolation Grammars are a declarative formalism with an operational semantics. Their goal is to emulate salient features of the human parser, and notably incrementality. The parsing process defined by GIGs incrementally builds a syntactic representation of a sentence as each successive lexeme is read. A GIG rule specifies a set of parse configurations that trigger its application and an operation to perform on a matching configuration. Rules are partly context-sensitive; furthermore, they are reversible, meaning that their operations can be undone, which allows the parsing process to be nondeterministic. These two factors confer enough expressive power to the formalism for parsing natural languages.
Graph interpolation can be viewed as an extension of tree adjunction to parse graphs. And, indeed, TAGs @cite_2 , by introducing a 2-dimensional formalism into computational linguistics, have made a decisive step towards designing a syntactic theory that is both computationally tractable and linguistically realistic. In this respect, it is an obligatory reference for any syntactic theory intent on satisfying these criteria.
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In this paper we present a general parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from recent linguistic work in TAGs (Abeille 1988).In our approach elementary structures are associated with their lexical heads. These structures specify extended domains of locality (as compared to a context-free grammar) over which constraints can be stated. These constraints either hold within the elementary structure itself or specify what other structures can be composed with a given elementary structure.We state the conditions under which context-free based grammars can be 'lexicalized' without changing the linguistic structures originally produced. We argue that even if one extends the domain of locality of CFGs to trees, using only substitution does not give the freedom to choose the head of each structure. We show how adjunction allows us to 'lexicalize' a CFG freely.We then show how a 'lexicalized' grammar naturally follows from the extended domain of locality of TAGs and present some of the linguistic advantages of our approach.A novel general parsing strategy for 'lexicalized' grammars is discussed. In a first stage, the parser builds a set structures corresponding to the input sentence and in a second stage, the sentence is parsed with respect to this set. The strategy is independent of the linguistic theory adopted and of the underlying grammar formalism. However, we focus our attention on TAGs. Since the set of trees needed to parse an input sentence is supposed to be finite, the parser can use in principle any search strategy. Thus, in particular, a top-down strategy can be used since problems due to recursive structures are eliminated. The parser is also able to use non-local information to guide the search.We then explain how the Earley-type parser for TAGs can be modified to take advantage of this approach. We describe the use of energy function optimization in very shallow syntactic parsing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpus-based n-grams of syntactic tags. The success of the hybrid syntactic disambiguator is evaluated against a held-out benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated. INTEX is a text processor; it is usually used to parse corpora of several megabytes. It includes several built-in large coverage dictionaries and grammars represented by graphs; the user may add his her own dictionaries and grammars. These tools are applied to texts in order to locate lexical and syntactic patterns, remove ambiguities, and tag words. INTEX builds concordances and indexes of all types of patterns; it is used by linguists to analyse corpora, but can also be viewed as an information retrieval system. Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU. We present the Potsdam systems that participated in the semantic dependency parsing shared task of SemEval 2014. They are based on linguistically motivated bidirectional transformations between graphs and trees and on utilization of syntactic dependency parsing. They were entered in both the closed track and the open track of the challenge, recording a peak average labeled F1 score of 78.60. In this paper, a tree generating system called a tree adjunct grammar is described and its formal properties are studied relating them to the tree generating systems of Brainerd (Information and Control14 (1969), 217-231) and Rounds (Mathematical Systems Theory 4 (1970), 257-287) and to the recognizable sets and local sets discussed by Thatcher (Journal of Computer and System Sciences1 (1967), 317-322; 4 (1970), 339-367) and Rounds. Linguistic relevance of these systems has been briefly discussed also. Abstract-like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. We couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. We also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. We treat the allocation of modifiers to heads as a weighted bipartite graph matching (or assignment) problem, a well studied problem in graph theory. Using BLEU to measure performance on a string regeneration task, we found an improvement, illustrating the benefit of the spanning tree approach armed with an argument satisfaction model. The field of grammatical inference (also known as grammar induction) is transversal to a number of research areas including machine learning, formal language theory, syntactic and structural pattern recognition, computational linguistics, computational biology and speech recognition. There is no uniform literature on the subject and one can find many papers with original definitions or points of view. This makes research in this subject very hard, mainly for a beginner or someone who does not wish to become a specialist but just to find the most suitable ideas for his own research activity. The goal of this paper is to introduce a certain number of papers related with grammatical inference. Some of these papers are essential and should constitute a common background to research in the area, whereas others are specialized on particular problems or techniques, but can be of great help on specific tasks. The current state-of-the-art end-to-end semantic role labeling (SRL) model is a deep neural network architecture with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL, suggesting that neural network models could see great improvements from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a new neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech, predicate detection and SRL. For example, syntax is incorporated by training one of the attention heads to attend to syntactic parents for each token. Our model can predict all of the above tasks, but it is also trained such that if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on the CoNLL-2005 SRL dataset LISA achieves an increase of 2.5 F1 absolute over the previous state-of-the-art on newswire with predicted predicates and more than 2.0 F1 on out-of-domain data. On ConLL-2012 English SRL we also show an improvement of more than 3.0 F1, a 13 reduction in error. This second edition of Grune and Jacobs brilliant work presents new developments and discoveries that have been made in the field. Parsing, also referred to as syntax analysis, has been and continues to be an essential part of computer science and linguistics. Parsing techniques have grown considerably in importance, both in computer science, ie. advanced compilers often use general CF parsers, and computational linguistics where such parsers are the only option. They are used in a variety of software products including Web browsers, interpreters in computer devices, and data compression programs; and they are used extensively in linguistics. In this paper we explore the idea of characterizing sentences by the shapes of their structural descriptions only; for example, in the case of context free grammars, by the shapes of the derivation trees only. Such structural descriptions will be called skeletons . A skeleton exhibits all of the grouping structure (phrase structure) of the sentence without naming the syntactic categories used in the description. The inclusion of syntactic categories as variables is primarily a question of economy of description. Every context free grammar is strongly equivalent to a skelet al grammar, in a sense made precise in the paper. Besides clarifying the role of skeletons in mathematical linguistics, we show that skelet al automata provide a characterization of local sets, remedying a “defect” in the usual tree automata theory. We extend the method of skelet al structural descriptions to other forms of tree describing systems. We also suggest a theoretical basis for grammatical inference based on grouping structure only. In this shared task paper for SemEval2014 Task 8, we show that most semantic structures can be approximated by trees through a series of almost bijective graph transformations. We transform input graphs, apply off-the-shelf methods from syntactic parsing on the resulting trees, and retrieve output graphs. Using tree approximations, we obtain good results across three semantic formalisms, with a 15.9 error reduction over a stateof-the-art semantic role labeling system on development data. Our system came in 3 6 in the shared task closed track. We develop a parameterized complexity theory for counting problems. As the basis of this theory, we introduce a hierarchy of parameterized counting complexity classes #W[t], for t spl ges 1, that corresponds to Downey and Fellows' (1999) W-hierarchy and show that a few central W-completeness results for decision problems translate to #W-completeness results for the corresponding counting problems. Counting complexity gets interesting with problems whose decision version is tractable, but whose counting version is hard. Our main result states that counting cycles and paths of length k in both directed and undirected graphs, parameterized by k, are #W[1]-complete. This makes it highly unlikely that any of these problems is fixed-parameter tractable, even though their decision versions are. More explicitly, our result shows that most likely there is no f(k) spl middot n sup c -algorithm for counting cycles or paths of length k in a graph of size n for any computable function f: spl Nopf spl rarr spl Nopf and constant c, even though there is a 2 sup O(k) spl middot n sup 2.376 algorithm for finding a cycle or path of length k (2). We develop the syntactic topic model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, which combines the semantic insights of topic models with the syntactic information available from parse trees. Each word of a sentence is generated by a distribution that combines document-specific topic weights and parse-tree-specific syntactic transitions. Words are assumed to be generated in an order that respects the parse tree. We derive an approximate posterior inference method based on variational methods for hierarchical Dirichlet processes, and we report qualitative and quantitative results on both synthetic data and hand-parsed documents. In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances (called the linguistic structure), a structure of purposes (called the intentional structure), and the state of focus of attention (called the attentional state). The linguistic structure consists of segments of the discourse into which the utterances naturally aggregate. The intentional structure captures the discourse-relevant purposes, expressed in each of the linguistic segments as well as relationships among them. The attentional state is an abstraction of the focus of attention of the participants as the discourse unfolds. The attentional state, being dynamic, records the objects, properties, and relations that are salient at each point of the discourse. The distinction among these components is essential to provide an adequate explanation of such discourse phenomena as cue phrases, referring expressions, and interruptions.The theory of attention, intention, and aggregation of utterances is illustrated in the paper with a number of example discourses. Various properties of discourse are described, and explanations for the behavior of cue phrases, referring expressions, and interruptions are explored.This theory provides a framework for describing the processing of utterances in a discourse. Discourse processing requires recognizing how the utterances of the discourse aggregate into segments, recognizing the intentions expressed in the discourse and the relationships among intentions, and tracking the discourse through the operation of the mechanisms associated with attentional state. This processing description specifies in these recognition tasks the role of information from the discourse and from the participants' knowledge of the domain. In this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. The supertags are designed such that only those elements on which the lexical item imposes constraints appear within a given supertag. Further, each lexical item is associated with as many supertags as the number of different syntactic contexts in which the lexical item can appear. This makes the number of different descriptions for each lexical item much larger than when the descriptions are less complex, thus increasing the local ambiguity for a parser. But this local ambiguity can be resolved by using statistical distributions of supertag co-occurrences collected from a corpus of parses. We have explored these ideas in the context of the Lexicalized Tree-Adjoining Grammar (LTAG) framework. The supertags in LTAG combine both phrase structure information and dependency information in a single representation. Supertag disambiguation results in a representation that is effectively a parse (an almost parse), and the parser need "only" combine the individual supertags. This method of parsing can also be used to parse sentence fragments such as in spoken utterances where the disambiguated supertag sequence may not combine into a single structure. Syntactic structures, by their nature, reflect first and foremost the formal constructions used for expressing meanings. This renders them sensitive to formal variation both within and across languages, and limits their value to semantic applications. We present UCCA, a novel multi-layered framework for semantic representation that aims to accommodate the semantic distinctions expressed through linguistic utterances. We demonstrate UCCA’s portability across domains and languages, and its relative insensitivity to meaning-preserving syntactic variation. We also show that UCCA can be effectively and quickly learned by annotators with no linguistic background, and describe the compilation of a UCCAannotated corpus. We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm, resulting in a more accurate AMR parser overall. Our parser yields an improvement of 5 absolute in F-measure over the best previous result. Second, the actions that we design are linguistically intuitive and capture the regularities in the mapping between the dependency structure and the AMR of a sentence. Third, our parser runs in nearly linear time in practice in spite of a worst-case complexity ofO(n 2 ). We describe a parsing approach that makes use of the perceptron algorithm, in conjunction with dynamic programming methods, to recover full constituent-based parse trees. The formalism allows a rich set of parse-tree features, including PCFG-based features, bigram and trigram dependency features, and surface features. A severe challenge in applying such an approach to full syntactic parsing is the efficiency of the parsing algorithms involved. We show that efficient training is feasible, using a Tree Adjoining Grammar (TAG) based parsing formalism. A lower-order dependency parsing model is used to restrict the search space of the full model, thereby making it efficient. Experiments on the Penn WSJ treebank show that the model achieves state-of-the-art performance, for both constituent and dependency accuracy. In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as "socially dominant, " "energetic, " and "trustworthy, " and propose a hierarchical model that builds on the layer of syntactical attributes, such as "smile" and "waving hand, " to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1, 124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of investigations around mediated images, intersecting with media analysis, psychology, and political communication.