Articles

We present an algorithm for generating referring expressions in open domains. Existing algorithms work at the semantic level and assume the avail- ability of a classification for attributes, which is only feasible for restricted domains. Our alterna- tive works at the realisation level, relies on Word- Net synonym and antonym …...

This paper presents SemFrame, a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many language- based applications. When compared to the handcrafted FrameNet, SemFrame achieves its best recall-precision balance with 83.2% recall (based …...

We present the first algorithm that computes opti- mal orderings of sentences into a locally coherent discourse. The algorithm runs very efficiently on a variety of coherence measures from the literature. We also show that the discourse ordering problem is NP-complete and cannot be approximated. 1...

In this paper we present a methodology for ex- tracting subcategorisation frames based on an automatic LFG f-structure annotation algorithm for the Penn-II Treebank. We extract abstract syntactic function-based subcategorisation frames (LFG semantic forms), traditional CFG category- based subcategorisation frames as well as mixed function/category-based frames, with or without preposition …...

We examine the utility of speech and lexical fea- tures for predicting student emotions in computer- human spoken tutoring dialogues. We first anno- tate student turns for negative, neutral, positive and mixed emotions. We then extract acoustic-prosodic features from the speech signal, and lexical items from the transcribed or recognized …...

We use machine learners trained on a combina- tion of acoustic confidence and pragmatic plausi- bility features computed from dialogue context to predict the accuracy of incoming n-best recogni- tion hypotheses to a spoken dialogue system. Our best results show a 25% weighted f-score improve- ment over a baseline system …...

We present a linguistically-motivated algorithm for recon- structing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. We use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. We find that our algo- rithm …...

In this paper we have designed and experi- mented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process struc- tured representations. Support Vector Ma- chines (SVMs), using a combination of such ker- nels and the flat feature kernel, classify Prop- Bank predicate …...

We describe a method for enriching the output of a parser with information available in a corpus. The method is based on graph rewriting using memory- based learning, applied to dependency structures. This general framework allows us to accurately re- cover both grammatical and semantic information as well as non-local …...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically gen- erated noisy data for training within …...

This paper discusses the application of the Expectation-Maximization (EM) clustering algorithm to the task of Chinese verb sense discrimination. The model utilized rich linguistic features that capture predicate- argument structure information of the target verbs. A semantic taxonomy for Chinese nouns, which was built semi-automatically based on two electronic Chinese …...

We describe two probabilistic models for unsuper- vised word-sense disambiguation using parallel cor- pora. The first model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense in- ventory for the target language, and recasts their ap- …...

Sentiment analysis seeks to identify the view- point(s) underlying a text span; an example appli- cation is classifying a movie review as “thumbs up” or “thumbs down”. To determine this sentiment po- larity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the …...

This paper shows how finite approximations of long distance dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automati- cally for the Penn-II treebank trees and use them …...

A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while …...

Sentiment classification is the task of labeling a re- view document according to the polarity of its pre- vailing opinion (favorable or unfavorable). In ap- proaching this problem, a model builder often has three sources of information available: a small col- lection of labeled documents, a large collection of unlabeled …...

We show that a practical translation of MRS de- scriptions into normal dominance constraints is fea- sible. We start from a recent theoretical translation and verify its assumptions on the outputs of the En- glish Resource Grammar (ERG) on the Redwoods corpus. The main assumption of the translation— that all …...

We present the first application of the head-driven statistical parsing model of Collins (1999) as a si- multaneous language model and parser for large- vocabulary speech recognition. The model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. The parser uses …...

This paper introduces an indexing method based on static analysis of grammar rules and type signatures for typed feature structure grammars (TFSGs). The static analysis tries to predict at compile-time which feature paths will cause unification failure during parsing at run-time. To support the static analysis, we introduce a new …...

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into …...