Articles

We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences writ- ten in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) or- ganized within Semeval 2013 (Negri et al., …...

This paper describes the evaluation of differ- ent kinds of textual features for the Cross- Lingual Textual Entailment Task of SemEval 2013. We have counted the number of N- grams for three types of textual entities (char- acter, word and PoS tags) that exist in the pair of sentences from …...

Web pages segmentation for document selection in Question Answering In this paper, we study two different kinds of web pages segmentation for document selection in question answering. The segmentation is applied prior to indexation in addition to the traditionnal passage retrieval step in question answering. In both cases, the segmentation …...

A fully discriminative training framework for Statistical Machine Translation A major pitfall of existing statistical machine translation systems is their lack of a proper training procedure. In fact, the phrase extraction and scoring processes that underlie the construction of the translation model typically rely on a chain of crude heuristics, …...

Semantic Annotation in Specific Domains with rich Ontologies Technical documentations are generally difficult to explore and maintain. Powerful tools can help, but they require that the documents have been semantically annotated. The annotations must be sufficiently specialized, rich and consistent. They must rely on some explicit semantic model – usually …...

Mining Partial Annotation Rules for Named Entity Recognition During the last decades, the unremitting increase of numeric data available has led to a more and more urgent need for efficient solution of information retrieval (IR). This paper concerns a problematic of first importance for the IR on linguistic data : …...

AbstractWe develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a …...

AbstractJoint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages. Starting from a transition-based model for joint part-of-speech tagging and dependency parsing, we explore different ways of integrating morphological features into the model. We also investigate the use of rule-based morphological …...

AbstractGreedy transition-based parsers are very fast but tend to suffer from error propagation. This problem is aggravated by the fact that they are normally trained using oracles that are deterministic and incomplete in the sense that they assume a unique canonical path through the transition system and are only valid …...

AbstractWe introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to …...

AbstractRecognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference …...

AbstractDistant supervision algorithms learn information extraction models given only large readily available databases and text collections. Most previous work has used heuristics for generating labeled data, for example assuming that facts not contained in the database are not mentioned in the text, and facts in the database must be mentioned …...

AbstractThere have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between …...

AbstractGreat writing is rare and highly admired. Readers seek out articles that are beautifully written, informative and entertaining. Yet information-access technologies lack capabilities for predicting article quality at this level. In this paper we present first experiments on article quality prediction in the science journalism domain. We introduce a corpus …...

AbstractDefining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting the input permutation space. We propose a method to dynamically shape such space and, thus, capture long-range word …...

AbstractWe present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo …...

AbstractAdjectives like good, great, and excellent are similar in meaning, but differ in intensity. Intensity order information is very useful for language learners as well as in several NLP tasks, but is missing in most lexical resources (dictionaries, WordNet, and thesauri). In this paper, we present a primarily unsupervised approach …...

AbstractHead splitting techniques have been successfully exploited to improve the asymptotic runtime of parsing algorithms for projective dependency trees, under the arc-factored model. In this article we extend these techniques to a class of non-projective dependency trees, called well-nested dependency trees with block-degree at most 2, which has been previously …...

AbstractThis paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a …...

AbstractIn current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture …...