We propose UDP , the first training-free parser for Universal Dependencies ( UD ) . Our algorithm is based on PageRank and a small set of head attachment rules . It features two-step decoding to guarantee that function words are attached as leaf nodes . The parser requires no training , and it is competitive with a delexicalized transfer system . UDP offers a linguistically sound unsupervised alternative to cross-lingual parsing for UD , which can be used as a baseline for such systems . The parser has very few parameters and is distinctly robust to domain change across languages . Grammar induction and unsupervised dependency parsing are active fields of research in natural language processing ( Klein and Manning , 2004 ; Gelling et al . , 2012 ) . However , many data-driven approaches struggle with learning relations that match the conventions of the test data , e.g. , Klein and Manning reported the tendency of their DMV parser to make determiners the heads of German nouns , which would not be an error if the test data used a DP analysis ( Abney , 1987 ) . Even supervised transfer approaches ( McDonald et al . , 2011 ) suffer from target adaptation problems when facing word order differences . The Universal Dependencies ( UD ) project ( Nivre et al . , 2015 ; Nivre et al . , 2016 ) offers a dependency formalism that aims at providing a consistent representation across languages , while enforcing a few hard constraints . The arrival of such treebanks , expanded and improved on a regular basis , provides a new milestone for crosslingual dependency parsing research ( McDonald et al . , 2013 ) . Furthermore , given that UD rests on a series of simple principles like the primacy of lexical heads , cf . Johannsen et al . ( 2015 ) for more details , we expect that such a formalism lends itself more naturally to a simple and linguistically sound rulebased approach to cross-lingual parsing . In this paper we present such an approach . Our system is a dependency parser that requires no training , and relies solely on explicit part-ofspeech ( POS ) constraints that UD imposes . In particular , UD prescribes that trees are single-rooted , and that function words like adpositions , auxiliaries , and determiners are always dependents of content words , while other formalisms might treat them as heads ( De Marneffe et al . , 2014 ) . We ascribe our work to the viewpoints of Bender ( 2009 ) about the incorporation of linguistic knowledge in language-independent systems . We have presented UDP , an unsupervised dependency parser for Universal Dependencies ( UD ) that makes use of personalized PageRank and a small set of head-dependent rules . The parser requires no training data and estimates adposition direction directly from the input . We achieve competitive performance on all but two UD languages , and even beat a multi-source delexicalized parser ( MSD ) on Hindi . We evaluated the parser on three POS setups and across domains . Our results show that UDP is less affected by deteriorating POS tags than MSD , and is more resilient to domain changes . Given how much of the overall dependency structure can be explained by this fairly system , we propose UDP as an additional UD parsing baseline . The parser , the in-house annotated test sets , and the domain data splits are made freely available . 6UD is a running project , and the guidelines are bound to evolve overtime . Indeed , the UD 2.0 guidelines have been recently released . UDP can be augmented with edge labeling for some deterministic labels like case or det . Some further constrains can be incorporated in UDP . Moreover , the parser makes no special treatment of multiword expression that would require a lexicon , coordinations or proper names . All these three kinds of structures have a flat tree where all words depend on the leftmost one . While coordination attachment is a classical problem in parsing and out of the scope of our work , a proper name sequence can be straightforwardly identified from the partof-speech tags , and it falls thus in the area of structures predictable using simple heuristics . Moreover , our use of PageRank could be expanded to directly score the potential dependency edges instead of words , e.g. , by means of edge reification .
For dependency parsing, unsupervised methods struggle with learning relations that match conventions of the test data and supervised counterparts suffer from word order target adaptation.
They propose an unsupervised approach based on PageRank and a set of head attachment rules that solely depend on explicit part-of-speech constraints from Universal Dependencies.
The proposed linguistically sound method performs competitively with a delexicalized transfer system while having few parameters and robustness to domain changes across languages.