Abstract not found

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

This paper describes the Stanford submission to the TAC 2008 RTE track.

We present an approach to textual entailment recognition, in which inference is based on a shallow semantic representation of relations (predicates and their arguments) in the text and hypothesis of the entailment pair, and in which specialized knowledge is encapsulated in modular components with very simple interfaces. We propose an architecture designed to integrate different, unscaled Natural Language Processing resources, and demonstrate an alignment-based method for combining them. We clarify the purpose of alignment in the RTE task, identifying two distinct alignment models, each of which leads to a different type of entailment system. We identify desirable properties of alignment, and use this to inform our implementation of an alignment component. We evaluate the resulting system on the RTE5 data set, and use an ablation study to assess the conformance of our alignment approach with these desired characteristics. 1

A range of Natural Language Processing tasks involve making judgments about the semantic relatedness of a pair of sentences, such as Recognizing Textual Entailment (RTE) and answer selection for Question Answering (QA). A key challenge that these tasks face in common is the lack of explicit alignment annotation between a sentence pair. We capture the alignment by using a novel probabilistic model that models tree-edit operations on dependency parse trees. Unlike previous tree-edit models which require a separate alignment-finding phase and resort to ad-hoc distance metrics, our method treats alignments as structured latent variables, and offers a principled framework for incorporating complex linguistic features. We demonstrate the robustness of our model by conducting experiments for RTE and QA, and show that our model performs competitively on both tasks with the same set of general features. 1

This paper proposes a general learning framework for a class of problems that require learning over latent intermediate representations. Many natural language processing (NLP) decision problems are defined over an expressive intermediate representation that is not explicit in the input, leaving the algorithm with both the task of recovering a good intermediate representation and learning to classify correctly. Most current systems separate the learning problem into two stages by solving the first step of recovering the intermediate representation heuristically and using it to learn the final classifier. This paper develops a novel joint learning algorithm for both tasks, that uses the final prediction to guide the selection of the best intermediate representation. We evaluate our algorithm on three different NLP tasks – transliteration, paraphrase identification and textual entailment – and show that our joint method significantly improves performance. 1

In this paper we introduce Statement Map, a project designed to help users navigate the vast amounts of information on the internet and come to informed opinions on topics of interest. It does this by mining the Web for a variety of viewpoints and presenting them to users together with supporting evidence in a way that makes it clear how the viewpoints are related. In this paper, we discuss the need to address issues of information credibility on the internet, outline the development of Statement Map generators for Japanese and English, discuss the technical issues that are being addressed, and report on the construction of the resources necessary to meet the project’s goals.

analysis of semantic similarity in comparable text

Monolingual alignment is frequently required for natural language tasks that involve similar or comparable sentences. We present a new model for monolingual alignment in which the score of an alignment decomposes over both the set of aligned phrases as well as a set of aligned dependency arcs. Optimal alignments under this scoring function are decoded using integer linear programming while model parameters are learned using standard structured prediction approaches. We evaluate our joint aligner on the Edinburgh paraphrase corpus and show significant gains over a Meteor baseline and a state-of-the-art phrase-based aligner. TITLE AND ABSTRACT IN FRENCH Un modèle de phrases et de dépendances pour l’alignement des paraphrases L’alignement monolingue s’impose fréquemment dans les tâches de langue naturelle qui comprennent des phrases similaires. Nous présentons un nouveau modèle pour l’alignement monolingue dans lequel le score d’un alignement tient compte de l’ensemble de phrases alignées et d’un ensemble d’arcs de dépendance alignés. Cette fonction de score donne des alignements en utilisant l’optimisation linéaire, et nous effectuons l’apprentissage des paramètres du modèle avec des méthodes standardes de prédiction structurée. Nous évaluons notre système mixte par rapport au corpus de paraphrases d’Edinburgh et nous démonstron un avantage significatif par rapport á Meteor et á un système de pointe fondé sur l’alignement des phrases.

Predicting entailment between two given texts is an important task upon which the performance of numerous NLP tasks depend on such as question answering, text summarization, and information extraction. The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F-score of 46.48 on the RTE-7 benchmark dataset. The proposed method is ranked 2-nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.