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With traditional Question Answering (QA) systems having reached nearly satisfactory performance, an emerging challenge is the development of successful Interactive Question Answering (IQA) systems. Important IQA subtasks are the identification of a dialogue-dependent typology of Follow Up Questions (FU Qs), automatic detection of the identified types, and the development of different context fusion strategies for each type. In this paper, we show how a system relying on shallow cues to similarity between utterances in a narrow dialogue context and other simple information sources, embedded in a machine learning framework, can improve FU Q answering performance by implicitly detecting different FU Q types and learning different context fusion strategies to help re-ranking their candidate answers. [ABSTRACT FROM AUTHOR]

Relational database (DB) management systems provide the standard means for structuring and querying large amounts of data. However, to access such data the exact structure of the DB must be know, and such a structure might be far from the conceptualization of a human being of the stored information. Ontologies help to bridge this gap, by providing a high level conceptual view of the information stored in a DB in a cognitively more natural way. Even in this setting, casual end users might not be familiar with the formal languages required to query ontologies. In this paper we address this issue and study the problem of ontology-based data access by means of natural language questions instead of queries expressed in some formal language. Specifically, we analyze how complex real life questions are and how far from the query languages accepted by ontology-based data access systems, how we can obtain the formal query representing a given natural language question, and how can we handle those questions which are too complex wrt the accepted query language. [ABSTRACT FROM AUTHOR]

Categorial grammars in the tradition of Lambek [1,2] are asymmetric: sequent statements are of the form ${\Gamma}\Rightarrow{A}$, where the succedent is a single formula A, the antecedent a structured configuration of formulas A1,...,An. The absence of structural context in the succedent makes the analysis of a number of phenomena in natural language semantics problematic. A case in point is scope construal: the different possibilities to build an interpretation for sentences containing generalized quantifiers and related expressions. In this paper, we explore a symmetric version of categorial grammar based on work by Grishin [3]. In addition to the Lambek product, left and right division, we consider a dual family of type-forming operations: coproduct, left and right difference. Communication between the two families is established by means of structure-preserving distributivity principles. We call the resulting system LG. We present a Curry-Howard interpretation for derivations. Our starting point is Curien and Herbelin's sequent system for λμ calculus [4] which capitalizes on the duality between logical implication (i.e. the Lambek divisions under the formulas-as-types perspective) and the difference operation. Importing this system into categorial grammar requires two adaptations: we restrict to the subsystem where linearity conditions are in effect, and we refine the interpretation to take the left-right symmetry and absence of associativity/commutativity into account. We discuss the continuation-passing-style (CPS) translation, comparing the call-by-value and call-by-name evaluation regimes. We show that in the latter (but not in the former) the types of LG are associated with appropriate denotational domains to enable a proper treatment of scope construal. [ABSTRACT FROM AUTHOR]