This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.

Resumen:The system for semantic evaluation VENSES (Venice Semantic Evaluation System) is organized as a pipeline of two subsystems: the first is a reduced version of GETARUN, our system for Text Understanding. The output of the system is a flat list of head-dependent structures (HDS) with Grammatical Relations (GRs) and Semantic Roles (SRs) labels. The evaluation system is made up of two main modules: the first is a sequence of linguistic rule-based subcalls; the second is a quantitatively based measurement of input structures. VENSES measures semantic similarity which may range from identical linguistic items, to synonymous or just morphologically derivable. Both modules go through General Consistency checks which are targeted to high level semantic attributes like presence of modality, negation, and opacity operators, temporal and spatial location checks. Results in cws, accuracy and precision are homogenoues for both training and test corpus and fare higher than 60%.

We present VENSES, a linguistically-based approach for semantic inference which is built around a neat division of labour between two main components: a grammatically-driven subsystem which is responsible for the level of predicatearguments well-formedness and works on the output of a deep parser that produces augmented head-dependency structures. A second subsystem fires allowed logical and lexical inferences on the basis of different types of structural transformations intended to produce a semantically valid meaning correspondence. In the current challenge, we produced a new version of the system, where we do away with grammatical relations and only use semantic roles to generate weighted scores. We also added a number of additional modules to cope with fine-grained inferential triggers which were not present in previous dataset. Different levels of argumenthood have been devised in order to cope with semantic uncertainty generated by nearly-inferrable Text-Hypothesis pairs where the interpretation needs reasoning. RTE3 has introduced texts of paragraph length: in turn this has prompted us to upgrade VENSES by the addition of a discourse level anaphora resolution module, which is paramount to allow entailment in pairs where the relevant portion of text contains pronominal expressions. We present the system, its relevance to the task at hand and an evaluation.

Abstract. Abstract summarization of conversations is a very challenging task that requires full understanding of the dialog turns, their roles and relationships in the conversations. We present an efficient system, derived from a fullyfledged text analysis system that performs the necessary linguistic analysis of turns in conversations and provides useful argumentative labels to build synthetic abstractive summaries of conversations.

In this paper we will present work carried out to scale up the system for text understanding called GETARUNS, and port it to be used in dialogue understanding. We will present the adjustments we made in order to cope with transcribed spoken dialogues like those produced in the ICSI Berkely project. In a final section we present preliminary evaluation of the system on non-referential pronominals individuation.