Articles

Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: native models.generative and discrimi- The former is more ro- bust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the …...

Building on work detecting errors in de- pendency annotation, we set out to correct local dependency errors. To do this, we outline the properties of annotation errors that make the task challenging and their existence problematic for learning. For the task, we define a feature-based model that explicitly accounts for …...

In this paper, we explore ways of improv- ing an inference rule collection and its ap- plication to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and ob- tain more rules using a hand-crafted lex- …...

We describe a methodology for learning a disambiguation model for deep pragmatic interpretations in the context of situated task-oriented dialogue. The system accu- mulates training examples for ambiguity resolution by tracking the fates of alter- native interpretations across dialogue, in- cluding subsequent clarificatory episodes initiated by the system itself. We …...

We present a method which, given a few words defining a concept in some lan- guage, retrieves, disambiguates and ex- tends corresponding terms that define a similar concept in another specified lan- guage. This can be very useful for cross-lingual information retrieval and the preparation of multi-lingual lexical re- sources. …...

In this paper, we first demonstrate the in- terest of the Loopy Belief Propagation al- gorithm to train and use a simple align- ment model where the expected marginal values needed for an efficient EM-training are not easily computable. We then im- prove this model with a distortion model based …...

In this paper, we present an efficient query selection algorithm for the retrieval of web text data to augment a statistical language model (LM). The number of retrieved rel- evant documents is optimized with respect to the number of queries submitted. The querying scheme is applied in the do- main …...

We present an algorithm for pronoun- anaphora (in English) that uses Expecta- tion Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails to find good models for the tasks to which it is set, in this case it works quite well. We …...

Sentence fluency is an important compo- nent of overall text readability but few studies in natural language processing have sought to understand the factors that define it. We report the results of an ini- tial study into the predictive power of sur- face syntactic statistics for the task; we use …...

This paper presents a feasibility study for im- plementing lexical morphology principles in a machine translation system in order to solve unknown words. Multilingual symbolic treat- ment of word-formation is seducing but re- quires an in-depth analysis of every step that has to be performed. The construction of a prototype …...

This paper describes a method using mor- phological rules and heuristics, for the au- tomatic extraction of large-coverage lexi- cons of stems and root word-forms from a raw text corpus. We cast the problem of high-coverage lexicon extraction as one of stemming followed by root word-form selection. We examine the …...

In this paper we present a human-based evaluation of surface realisation alterna- tives. We examine the relative rankings of naturally occurring corpus sentences and automatically generated strings chosen by statistical models (language model, log- linear model), as well as the naturalness of the strings chosen by the log-linear model. We …...

Sense induction seeks to automatically identify word senses directly from a cor- pus. A key assumption underlying pre- vious work is that the context surround- ing an ambiguous word is indicative of its meaning. Sense induction is thus typ- ically viewed as an unsupervised cluster- ing problem where the aim …...

In this paper, we present an integrated model of the two central tasks of dialog management: interpreting user actions and generating system actions. We model the interpretation task as a classication prob- lem and the generation task as a predic- tion problem. These two tasks are inter- leaved in an …...

Syntactic Reordering of the source lan- guage to better match the phrase struc- ture of the target language has been shown to improve the performance of phrase-based Statistical Machine Transla- tion. This paper applies syntactic reorder- ing to English-to-Arabic translation. It in- troduces reordering rules, and motivates them linguistically. It …...

Parallel Multiple Context-Free Grammar (PMCFG) is an extension of context-free grammar for which the recognition problem is still solvable in polynomial time. We describe a new parsing algorithm that has the advantage to be incremental and to support PMCFG directly rather than the weaker MCFG formal- ism. The algorithm is …...

Machine translation (MT) systems have improved significantly; however, their out- puts often contain too many errors to com- municate the intended meaning to their users. This paper describes a collabora- tive approach for mediating between an MT system and users who do not under- stand the source language and thus …...

We propose a system which builds, in a semi-supervised manner, a resource that aims at helping a NER system to anno- tate corpus-specific named entities. This system is based on a distributional ap- proach which uses syntactic dependen- cies for measuring similarities between named entities. The specificity of the presented …...

The lack of positive results on super- vised domain adaptation for WSD have cast some doubts on the utility of hand- tagging general corpora and thus devel- oping generic supervised WSD systems. In this paper we show for the first time that our WSD system trained on a general source …...

In this paper we propose a new graph- based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disam- biguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also …...