Articles

We describe a parser used in the CoNLL 2006 Shared Task, “Multingual Depen- dency Parsing.” The parser first identi- fies syntactic dependencies and then labels those dependencies using a maximum en- tropy classifier. We consider the impact of feature engineering and the choice of ma- chine learning algorithm, with particular …...

In this paper, we present a framework for multi-lingual dependency parsing. Our bottom-up deterministic parser adopts Nivre’s algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to determine the word dependency attachments. Then, a maxi- mum entropy method (MaxEnt) is used for determining the label of the …...

We describe an online learning depen- dency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experi- ment with a large feature set that mod- els: the tokens involved in dependencies and their immediate context, the surface- text distance between tokens, and the …...

This paper presents a Constraint Grammar- inspired machine learner and parser, Ling­ Pars, that assigns dependencies to morpho­ logically annotated treebanks in a function- centred way. The system not only bases at­ tachment probabilities for PoS, case, mood, lemma on those features' function probabili­ ties, but also uses topological features …...

Each year the Conference on Com- putational Natural Language Learning (CoNLL)1 features a shared task, in which participants train and test their systems on exactly the same data sets, in order to bet- ter compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multi- lingual Dependency Parsing. In …...

We present a novel context pattern in- duction method for information extrac- tion, specifically named entity extraction. Using this method, we extended several classes of seed entity lists into much larger high-precision lists. Using token member- ship in these extended lists as additional features, we improved the accuracy of a …...

Much work on information extraction has successfully used gazetteers to recognise uncommon entities that cannot be reliably identified from local context alone. Ap- proaches to such tasks often involve the use of maximum entropy-style models, where gazetteers usually appear as highly informative features in the model. Al- though such features …...

We present a simple context-free gram- matical inference algorithm, and prove that it is capable of learning an inter- esting subclass of context-free languages. We also demonstrate that an implementa- tion of this algorithm is capable of learn- ing auxiliary fronting in polar interroga- tives (AFIPI) in English. This has …...

Distributional approaches to grammar in- duction are typically inefficient, enumer- ating large numbers of candidate con- stituents. In this paper, we describe a simplified model of distributional analy- sis which uses heuristics to reduce the number of candidate constituents under consideration. We apply this model to a large corpus of …...

In this paper we investigate a new problem of identifying the perspective from which a document is written. By perspective we mean a point of view, for example, from the perspective of Democrats or Repub- licans. Can computers learn to identify the perspective of a document? Not every sentence is …...

We investigate the appropriateness of us- ing a technique based on support vector machines for identifying thematic struc- ture of text streams. The thematic seg- mentation task is modeled as a binary- classification problem, where the different classes correspond to the presence or the absence of a thematic boundary. Exper- …...

We propose a generalization of the super- vised DOP model to unsupervised learning. This new model, which we call U-DOP, initially assigns all possible unlabeled binary trees to a set of sentences and next uses all subtrees from (a large subset of) these binary trees to compute the most probable …...

This paper presents a new application of the recently proposed machine learning method Alternating Structure Optimiza- tion (ASO), to word sense disambiguation (WSD). Given a set of WSD problems and their respective labeled examples, we seek to improve overall performance on that set by using all the labeled exam- ples …...

This paper investigates whether human as- sociations to verbs as collected in a web experiment can help us to identify salient verb features for semantic verb classes. Assuming that the associations model as- pects of verb meaning, we apply a clus- tering to the verbs, as based on the as- …...

Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, we used syntactic sub- trees that span potential argument struc- tures of the target predicate in tree ker- nel functions. …...

We present a method for recognizing se- mantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its im- portance. We improve the …...

We demonstrate an original and success- ful approach for both resolving and gen- erating definite anaphora. We propose and evaluate unsupervised models for ex- tracting hypernym relations by mining co- occurrence data of definite NPs and po- tential antecedents in an unlabeled cor- pus. The algorithm outperforms a stan- dard …...

We explore a novel computational ap- proach to identifying “constructions” or “multi-word expressions” (MWEs) in an annotated corpus. In this approach, MWEs have no special status, but emerge in a general procedure for finding the best statistical grammar to describe the train- ing corpus. The statistical grammar for- malism used …...

We present an improved approach for learning dependency parsers from tree- bank data. Our technique is based on two ideas for improving large margin train- ing in the context of dependency parsing. First, we incorporate local constraints that enforce the correctness of each individ- ual link, rather than just scoring …...

While most work on parsing with PCFGs has focused on local correlations between tree configurations, we attempt to model non-local correlations using a finite mix- ture of PCFGs. A mixture grammar fit with the EM algorithm shows improve- ment over a single PCFG, both in parsing accuracy and in test …...