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Many different tagsets are used in existing corpora; these tagsets vary according to the objectives of specific projects (which may be as far apart as robust parsing vs. spelling correction). In many situations, however, one would like to have uniform access to the linguistic information encoded in corpus annotations without having to know the classification schemes in detail. This paper describes a tool which maps unstructured morphosyntactic tags to a constraint-based, typed, configurable specification language, a ``standard tagset. The mapping relies on a manually written set of mapping rules, which is automatically checked for consistency. In certain cases, unsharp mappings are unavoidable, and noise, i.e. groups of word forms {sl not} conforming to the specification, will appear in the output of the mapping. The system automatically detects such noise and informs the user about it. The tool has been tested with rules for the UPenn tagset cite{up} and the SUSANNE tagset cite{garside}, in the framework of the EAGLESfootnote{LRE project EAGLES, cf. cite{eagles}.} validation phase for standardised tagsets for European languages.
The ALMA Observation Support Tool (OST) is an ALMA simulator which is interacted with solely via a standard web browser. It is aimed at users who may or may not be experts in interferometry, or those that do not wish to familarise themselves with the
In this work, we present a web-based annotation and querying tool Sangrahaka. It annotates entities and relationships from text corpora and constructs a knowledge graph (KG). The KG is queried using templatized natural language queries. The applicati
Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of re
The design and development process for Internet of Things (IoT) applications is more complicated than for desktop, mobile, or web applications. IoT applications require both software and hardware to work together across multiple different types of no
Deep learning applications in shaping ad hoc planning proposals are limited by the difficulty in integrating professional knowledge about cities with artificial intelligence. We propose a novel, complementary use of deep neural networks and planning