A Mention-Ranking Model for Abstract Anaphora Resolution

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence--antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and -- if disregarding syntax -- discriminates candidates using deeper features.Comment: In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP). Copenhagen, Denmar

Text as scene: discourse deixis and bridging relations

En este artículo se presenta un nuevo marco, “el texto como escena”, que establece las bases para la anotación de dos relaciones de correferencia: la deixis discursiva y las relaciones de bridging. La incorporación de lo que llamamos escenas textuales y contextuales proporciona unas directrices de anotación más flexibles, que diferencian claramente entre tipos de categorías generales. Un marco como éste, capaz de tratar la deixis discursiva y las relaciones de bridging desde una perspectiva común, tiene como objetivo mejorar el bajo grado de acuerdo entre anotadores obtenido por esquemas de anotación anteriores, que son incapaces de captar las referencias vagas inherentes a estos dos tipos de relaciones. Las directrices aquí presentadas completan el esquema de anotación diseñado para enriquecer el corpus español CESS-ECE con información correferencial y así construir el corpus CESS-Ancora.This paper presents a new framework, “text as scene”, which lays the foundations for the annotation of two coreferential links: discourse deixis and bridging relations. The incorporation of what we call textual and contextual scenes provides more flexible annotation guidelines, broad type categories being clearly differentiated. Such a framework that is capable of dealing with discourse deixis and bridging relations from a common perspective aims at improving the poor reliability scores obtained by previous annotation schemes, which fail to capture the vague references inherent in both these links. The guidelines presented here complete the annotation scheme designed to enrich the Spanish CESS-ECE corpus with coreference information, thus building the CESS-Ancora corpus.This paper has been supported by the FPU grant (AP2006-00994) from the Spanish Ministry of Education and Science. It is based on work supported by the CESS-ECE (HUM2004-21127), Lang2World (TIN2006- 15265-C06-06), and Praxem (HUM2006- 27378-E) projects

Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government