We investigate the effect of corpus size in combining supervised and unsupervised learning for two types of attachment decisions: relative clause attachment and prepositional phrase attachment. The supervised component is Collins’ parser, trained on the Wall Street Journal. The unsupervised component gathers lexical statistics from an unannotated corpus of newswire text. We find that the combin...

We present the first results on parsing the SYNTAGRUS treebank of Russian with a data-driven dependency parser, achieving a labeled attachment score of over 82% and an unlabeled attachment score of 89%. A feature analysis shows that high parsing accuracy is crucially dependent on the use of both lexical and morphological features. We conjecture that the latter result can be generalized to richl...

Statistical methods for PP attachment fall into two classes according to the training material used: first, unsupervised methods trained on raw text corpora and second, supervised methods trained on manually disambiguated examples. Usually supervised methods win over unsupervised methods with regard to attachment accuracy. But what if only small sets of manually disambiguated material are avail...

This paper examines the use of an unsupervised statistical model for determining the attachment of ambiguous coordinate phrases (CP) of the form n1 p n2 cc n3. The model presented here is based on [AR98], an unsupervised model for determining prepositional phrase attachment. After training on unannotated 1988 Wall Street Journal text, the model performs at 72% accuracy on a development set from...

Arch et al. (2014) demonstrated that brief self-compassion meditation training (SCT) dampened sympathetic (salivary alpha-amylase) and subjective anxiety responses to the Trier Social Stress Test (TSST), relative to attention and no-instruction control conditions. The present study examined baseline predictors and moderators of these SCT intervention effects. Baseline characteristics included t...

In this paper we revisit the classical NLP problem of prepositional phrase attachment (PPattachment). Given the pattern V −NP1−P−NP2 in the text, where V is verb,NP1 is a noun phrase, P is the preposition and NP2 is the other noun phrase, the question asked is where does P −NP2 attach: V or NP1? This question is typically answered using both the word and the world knowledge. Word Sense Disambig...

We present a new algorithm for classification based on Markov random walks. We evaluate our method, TUMBL, on the ppattach prepositional phrase attachment data set and report top performance when the amount of training data is severely limited.

This paper describes work in progress that is identifying shortcomings of existing Prepositional Phrase (PP) attachment algorithms and producing a new resource derived from the Penn TreeBank (PTB) corpus. The aim is to use this new resource (PTB Prime) to improve the accuracy of PP attachment algorithms and use this in an existing text processing system (LaSIE-II).