Parsing as Reduction

We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin

Crossings as a side effect of dependency lengths

The syntactic structure of sentences exhibits a striking regularity: dependencies tend to not cross when drawn above the sentence. We investigate two competing explanations. The traditional hypothesis is that this trend arises from an independent principle of syntax that reduces crossings practically to zero. An alternative to this view is the hypothesis that crossings are a side effect of dependency lengths, i.e. sentences with shorter dependency lengths should tend to have fewer crossings. We are able to reject the traditional view in the majority of languages considered. The alternative hypothesis can lead to a more parsimonious theory of language.Comment: the discussion section has been expanded significantly; in press in Complexity (Wiley

Memory limitations are hidden in grammar

[Abstract] The ability to produce and understand an unlimited number of different sentences is a hallmark of human language. Linguists have sought to define the essence of this generative capacity using formal grammars that describe the syntactic dependencies between constituents, independent of the computational limitations of the human brain. Here, we evaluate this independence assumption by sampling sentences uniformly from the space of possible syntactic structures. We find that the average dependency distance between syntactically related words, a proxy for memory limitations, is less than expected by chance in a collection of state-of-the-art classes of dependency grammars. Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of non-linguistic cognitive constraints

Memory limitations are hidden in grammar

The ability to produce and understand an unlimited number of different sentences is a hallmark of human language. Linguists have sought to define the essence of this generative capacity using formal grammars that describe the syntactic dependencies between constituents, independent of the computational limitations of the human brain. Here, we evaluate this independence assumption by sampling sentences uniformly from the space of possible syntactic structures. We find that the average dependency distance between syntactically related words, a proxy for memory limitations, is less than expected by chance in a collection of state-of-the-art classes of dependency grammars. Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of non-linguistic cognitive constraints.Comment: Version improved with reviewer feedbac

Restricted Non-Projectivity: Coverage vs. Efficiency

[Abstract] In the last decade, various restricted classes of non-projective dependency trees have been proposed with the goal of achieving a good tradeoff between parsing efficiency and coverage of the syntactic structures found in natural languages. We perform an extensive study measuring the coverage of a wide range of such classes on corpora of 30 languages under two different syntactic annotation criteria. The results show that, among the currently known relaxations of projectivity, the best tradeoff between coverage and computational complexity of exact parsing is achieved by either 1-endpoint-crossing trees or MH k trees, depending on the level of coverage desired. We also present some properties of the relation of MH k trees to other relevant classes of trees.Ministerio de Economía y Competitividad; FFI2014-51978-C2-2-

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

A hierarchy of mildly context sensitive dependency grammar

The paper presents Colored Multiplanar Link Grammars (CMLG). These grammars are reducible to extended right-linear S-grammars (Wartena 2001) where the storage type S is a concatenation of c pushdowns. The number of colors available in these grammars induces a hierarchy of Classes of CMLGs. By fixing also another parameter in CMLGs, namely the bound t for non-projectivity depth, we get c-Colored t-Non-projective Dependency Grammars (CNDG) that generate acyclic dependency graphs. Thus, CNDGs form a two-dimensional hier- archy of dependency grammars. A part of this hierarchy is mildly context-sensitive and non-projective.The paper presents Colored Multiplanar Link Grammars (CMLG). These grammars are reducible to extended right-linear S-grammars (Wartena 2001) where the storage type S is a concatenation of c pushdowns. The number of colors available in these grammars induces a hierarchy of Classes of CMLGs. By fixing also another parameter in CMLGs, namely the bound t for non-projectivity depth, we get c-Colored t-Non-projective Dependency Grammars (CNDG) that generate acyclic dependency graphs. Thus, CNDGs form a two-dimensional hier- archy of dependency grammars. A part of this hierarchy is mildly context-sensitive and non-projective.Peer reviewe