Interaction Grammars

Interaction Grammar (IG) is a grammatical formalism based on the notion of polarity. Polarities express the resource sensitivity of natural languages by modelling the distinction between saturated and unsaturated syntactic structures. Syntactic composition is represented as a chemical reaction guided by the saturation of polarities. It is expressed in a model-theoretic framework where grammars are constraint systems using the notion of tree description and parsing appears as a process of building tree description models satisfying criteria of saturation and minimality

Word Ordering as a Graph Rewriting Process

International audienceThis paper shows how the correspondence between a unordered dependency tree and a sentence that expresses it can be achieved by transforming the tree into a string where each linear precedence link corresponds to one specific syntactic relation. We propose a formal grammar with a distributed architecture that can be used for both synthesis and analysis. We argue for the introduction of a topological tree as an intermediate step between dependency syntax and word order

On learning discontinuous dependencies from positive data

International audienceThis paper is concerned with learning in the model of Gold the Categorial Dependency Grammars (CDG), which express discontin- uous (non-projective) dependencies. We show that rigid and k-valued CDG (without optional and iterative types) are learnable from strings. In fact, we prove that the languages of dependency nets coding rigid CDGs have finite elasticity, and we show a learning algorithm. As a standard corollary, this result leads to the learnability of rigid or k- valued CDGs (without optional and iterative types) from strings

Encoding a syntactic dictionary into a super granular unification grammar

International audienceWe show how to turn a large-scale syntactic dictionary into a dependency-based unification grammar where each piece of lexical information calls a separate rule, yielding a super granular grammar. Subcategorization, raising and control verbs, auxiliaries and copula, passivization, and tough-movement are discussed. We focus on the semantics-syntax interface and offer a new perspective on syntactic structure

Monitasoisuus - malli puupankeissa olevia dependenssirakenteita varten

Cited several times. E.g. 1. Marco Kuhlmann & Joakim Nivre: Mildly non-projective dependency structures. In the Proceedings of the COLING/ACL on Main conference poster sessions, p. 507--514. In series COLING-ACL '06. Sydney, Australia, 2006. 2. Carlos Gómez-Rodriguez and Joakim Nivre: A transition-based for 2-Planar Dependency Structures. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1492--1501, Uppsala, Sweden, 11-16 July 2010. ACL 3. Marco Kuhlmann. Dependency Structures and Lexicalized Grammars. An Algebraic Approach. LNAI 6270. FoLLI Publications on Logic, Language and Information. Springer 2010. 4.Eri kielille tehtyjen puupankkien määrä kasvaa tasaista vauhtia. Huomattava osa viimeaikaisista puupankeista käyttää annotaatiokäytäntöä joka perustuu dependenssisyntaksiin. Esitämme tässä paperissa mallin lingvistisesti adekvaattien dependenssirakenteiden luokille. Malli on testattu Danish Dependency Treebankin avulla. jne...The number of treebanks available for different languages is growing steadily. A considerable portion of the recent treebanks use annotation schemes that are based on dependency syntax. In this paper, we give a model for linguistically adequate classes of dependency structures in treebanks. Our model is tested using the Danish Dependency Treebank. Lecerf’s projectivity hypothesis assumes a constraint on linear word- order in dependency analyses. Unfortunately, projectivity does not lend itself to adequate treatment of certain non-local syntactic phenomena which are extensively studied in the literature of constituent-based theories such as TG, GB, GPSG, TAG, and LFG. Among these phenomena are scrambling, topicalizations, WH-movements, cleft sentences, discontinuous NPs, and discontinuous negation. a few relaxed models somewhat similar to projectivity have been pro- posed. These include quasi-projectivity, planarity, pseudo-projectivity, meta-projectivity, and polarized dependency grammars. None of the these models is motivated by formal language theory. The current work presents a new word-order model with a clear connection to formal language theory. The model, multiplanarity with a bounded number of planes, is based on planarity, which is itself a generalization of projectivity.Peer reviewe

On learning discontinuous dependencies from positive data

International audienceThis paper is concerned with learning in the model of Gold the Categorial Dependency Grammars (CDG), which express discontin- uous (non-projective) dependencies. We show that rigid and k-valued CDG (without optional and iterative types) are learnable from strings. In fact, we prove that the languages of dependency nets coding rigid CDGs have finite elasticity, and we show a learning algorithm. As a standard corollary, this result leads to the learnability of rigid or k- valued CDGs (without optional and iterative types) from strings

Spurious ambiguity and focalization

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.Peer ReviewedPostprint (published version

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language