An optimizing compiler consists of a front end parsing a textual programming language into an intermediate representation (IR), a middle end performing optimizations on the IR, and a back end lowering the IR to a target representation (TR) built of operations supported by the target hardware. In modern compiler construction graph-based IRs are employed. Optimization and lowering tasks can then ...

We argue that Ariola and Felleisen's and Maraist, Odersky and Wadler's axiomatization of the call-by-need lambda calculus forms a suitable formal basis for tracing evaluation in lazy functional languages. In particular, it allows a one-dimensional textual representation of terms, rather than requiring a two-dimensional graphical representation using arrows. We describe a program LetTrace, imple...

Geographic Information Retrieval (GIR) has emerged as a new and promising tool for representation, storage, organisation of and access to geographic information. One of the current issues in GIR research is ranking of retrieved documents by both textual and geographic similarity measures. This paper describes an approach that learns GIR ranking functions using Genetic Programming (GP) methods b...

In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aim...

Interaction nets are graph rewriting systems which are a generalisation of proof nets for classical linear logic. The linear chemical abstract machine (CHAM) is a term rewriting system which corresponds to classical linear logic, via the Curry-Howard isomorphism. We can obtain a textual calculus for interaction nets which is surprisingly similar to linear CHAM based on the multiplicative fragme...

This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning...

In this paper, we present a learning framework for the semantic annotation of text documents that can be used as textual cases in case-based reasoning applications. The annotations are known as knowledge roles and are task-dependent. The framework relies on deep natural language processing techniques and does not require the existence of any domain-dependent resources. Several experiments are p...

Spreadsheets are widely used within Science, technology, engineering and maths education. Despite their widespread use, end-user spreadsheet errors are still extremely common and have been shown to have an adverse effect on learning. The textual representation of formulas can be particularly complex and error-prone, exacerbating barriers to dyslexic users. Our work focuses on the design and dev...

In this paper we will introduce a measure of saturation for unstructured texts of unknown domains. Therefore we will present the Textual Coverage Rate (TCR), a method to determine the IE coverage of unstructured texts using a given vocabulary. We advance efficiency while building vocabulary repositories tailored for given problems and ensure a certain quality of representation. Our approach, wh...

In this paper, we explore the classification of emotions in songs, using the music and the lyrics representation of the songs. We introduce a novel corpus of music and lyrics, consisting of 100 songs annotated for emotions. We show that textual and musical features can both be successfully used for emotion recognition in songs. Moreover, through comparative experiments, we show that the joint u...