A potential work item (PWI) for ISO standard (MAP) about linguistic annotation concerning syntax-semantics mapping is discussed. MAP is a framework for graphical linguistic annotation to specify a mapping (set of combinations) between possible syntactic and semantic structures of the annotated linguistic data. Just like a UML diagram, a MAP diagram is formal, in the sense that it accurately specifies such a mapping. MAP provides a diagrammatic sort of concrete syntax for linguistic annotation far easier to understand than textual concrete syntax such as in XML, so that it could better facilitate collaborations among people involved in research, standardization, and practical use of linguistic data. MAP deals with syntactic structures including dependencies, coordinations, ellipses, transsentential constructions, and so on. Semantic structures treated by MAP are argument structures, scopes, coreferences, anaphora, discourse relations, dialogue acts, and so forth. In order to simplify explicit annotations, MAP allows partial descriptions, and assumes a few general rules on correspondence between syntactic and semantic compositions.

This paper summarizes the latest, final version of ISO standard 24617-2 ``Semantic annotation framework, Part 2: Dialogue acts"""". Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of `ideal concrete syntax'. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.

This paper describes an ISO project which aims at developing a standard for annotating spoken and multimodal dialogue with semantic information concerning the communicative functions of utterances, the kind of semantic content they address, and their relations with what was said and done earlier in the dialogue. The project, ISO 24617-2 ""Semantic annotation framework, Part 2: Dialogue acts"", is currently at DIS stage. The proposed annotation schema distinguishes 9 orthogonal dimensions, allowing each functional segment in dialogue to have a function in each of these dimensions, thus accounting for the multifunctionality that utterances in dialogue often have. A number of core communicative functions is defined in the form of ISO data categories, available at http://semantic-annotation.uvt.nl/dialogue-acts/iso-datcats.pdf; they are divided into ""dimension-specific"" functions, which can be used only in a particular dimension, such as Turn Accept in the Turn Management dimension, and ""general-purpose"" functions, which can be used in any dimension, such as Inform and Request. An XML-based annotation language, ""DiAML"" is defined, with an abstract syntax, a semantics, and a concrete syntax.

In the domain of artificial intelligence, the pattern of information flow varies drastically from one context to another. To capture this diversity of information flow, a natural-language processing (NLP) system should consist of modules of constraints and one general constraint solver to process all of them; there should be no specialized procedure module such as a parser and a generator. This paper presents how to implement such a constraint-based approach to NLP. Dependency Propagation (DP) is a constraint solver which transforms the program (=constraint) represented in terms of logic programs. Constraint Unification (CU) is a unification method incorporating DP. cu-Prolog is an extended Prolog which employs CU instead of the standard unification. cu-Prolog can treat some lexical and grammatical knowledge as constraints on the structure of grammatical categories, enabling a very straightforward implementation of a parser using constraint-based grammars. By extending DP, one can deal efficiently with phrase structures in terms of constraints. Computation on category structures and phrase structures are naturally integrated in an extended DP. The computation strategies to do all this are totally attributed to a very abstract, task-independent principle: prefer computation using denser information. Efficient parsing is hence possible without any parser.