Antonella Bristot

Entailment and Anaphora Resolution in RTE3

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 predicate-arguments 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.

VENSES – a linguistically-based system for semantic evaluation

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%

Abstractive summarization of voice communications

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.

Scaling up a NLU system from text to dialogue understanding

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.