Articles

Traditionally, word sense disambiguation (WSD) involves a different context classifi- cation model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a word independent context pair classification model. Statistical models are not trained for classifying the word contexts, but for classi- fying …...

We propose an unsupervised Expectation Maximization approach to pronoun reso- lution. The system learns from a fixed list of potential antecedents for each pro- noun. We show that unsupervised learn- ing is possible in this context, as the per- formance of our system is comparable to supervised methods. Our results …...

Recent work on the problem of detect- ing synonymy through corpus analysis has used the Test of English as a Foreign Lan- guage (TOEFL) as a benchmark. How- ever, this test involves as few as 80 ques- tions, prompting questions regarding the statistical significance of reported results. We overcome this …...

We introduce a learning semantic parser, SCISSOR, that maps natural-language sen- tences to a detailed, formal, meaning- representation language. It first uses an integrated statistical parser to pro- duce a semantically augmented parse tree, in which each non-terminal node has both a syntactic and a semantic label. A compositional-semantics procedure …...

Research on document similarity has shown that complex representations are not more accurate than the simple bag-of- words. Term clustering, e.g. using latent semantic indexing, word co-occurrences or synonym relations using a word ontol- ogy have been shown not very effective. In particular, when to extend the similar- ity function …...

In order to achieve the long-range goal of semantic interpretation of noun com- pounds, it is often necessary to first de- termine their syntactic structure. This pa- per describes an unsupervised method for noun compound bracketing which extracts statistics from Web search engines using a χ2 measure, a new set …...

In this paper we present a new approach to controlling the behaviour of a natural lan- guage generation system by correlating in- ternal decisions taken during free generation of a wide range of texts with the surface sty- listic characteristics of the resulting outputs, and using the correlation to control …...

We describe a new sentence realization framework for text-to-text applications. This framework uses IDL-expressions as a representation formalism, and a gener- ation mechanism based on algorithms for intersecting IDL-expressions with proba- bilistic language models. We present both theoretical and empirical results concern- ing the correctness and efficiency of these algorithms. …...

This paper describes a novel system for acquiring adjectival subcategorization frames (SCFs) and associated frequency information from English corpus data. The system incorporates a decision-tree classifier for 30 SCF types which tests for the presence of grammatical relations (GRs) in the output of a robust statisti- cal parser. It uses …...

Previous work has used monolingual par- allel corpora to extract and generate para- phrases. We show that this task can be done using bilingual parallel corpora, a much more commonly available resource. Using alignment techniques from phrase- based statistical machine translation, we show how paraphrases in one language can be …...

This paper introduces a new method for identifying candidate phrasal terms (also known as multiword units) which applies a nonparametric, rank-based heuristic measure. Evaluation of this measure, the mutual rank ratio metric, shows that it produces better results than standard statistical measures when applied to this task. 1...

Despite much recent progress on accu- rate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label se- quence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependen- cies …...

Semantic role labeling is the process of annotating the predicate-argument struc- ture in text with semantic labels. In this paper we present a state-of-the-art base- line semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including fea- tures …...

In this paper, we explore the power of randomized algorithm to address the chal- lenge of working with very large amounts of data. We apply these algorithms to gen- erate noun similarity lists from 70 million pages. We reduce the running time from quadratic to practically linear in the num- …...

We present an approach to using a mor- phological analyzer for tokenizing and morphologically tagging (including part- of-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We …...

In this paper, we present a novel training method for a localized phrase-based predic- tion model for statistical machine translation (SMT). The model predicts blocks with orien- tation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses real- valued …...

This paper describes a novel instance- based sentence boundary determination method for natural language generation that optimizes a set of criteria based on examples in a corpus. Compared to exist- ing sentence boundary determination ap- proaches, our work offers three signifi- cant contributions. First, our approach provides a general domain …...

In this paper, we propose a new context- dependent SMT model that is tightly cou- pled with a language model. It is de- signed to decrease the translation ambi- guities and efficiently search for an opti- mal hypothesis by reducing the hypothe- sis search space. It works through recipro- cal …...

Syntax-based statistical machine transla- tion (MT) aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical ma- chine translation system based on a prob- abilistic synchronous dependency insertion grammar. Synchronous depend- ency insertion grammars are a version of synchronous grammars defined on de- pendency …...

We describe a method for incorporating syntactic informa- tion in statistical machine translation systems. The first step of the method is to parse the source language string that is be- ing translated. The second step is to apply a series of trans- formations to the parse tree, effectively reordering the …...