560 research outputs found
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H2 2 has universal Turing expressivity though H2 2 is decidable given a finite signature. Additionally we show that Knuth–Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation MetagolD to PAC-learn minimal cardinality H2 2 definitions. This result is consistent with our experiments which indicate that MetagolD efficiently learns compact H2 2 definitions involving predicate invention for learning robotic strategies, the East–West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper
Experimental Investigation into the Influence of Backfill Types on the Vibro-acoustic Characteristics of Leaks in MDPE Pipe
Pipe leak location estimates are commonly conducted using Vibro-Acoustic Emission (VAE) based methods, usually using accelerometers or hydrophones. Successful estimation of a leak's location is dependent on a number of factors, including the speed of sound, resonance, backfill, reflections from other sources, leak shape and size. However, despite some investigation into some of the aforementioned factors, the influence of backfill type on a leak's VAE signal has still not been experimentally quantified. A limited number of studies have attempted to quantify the effects of backfill. However, all of these studies couple other variables which could be equally responsible for their observed changes in leak signal. There have been no controlled studies where one variable can be directly compared to one another (i.e. all variables remain constant, only changing backfill type). The aim of this paper is to better characterise the influence of backfill on a leak's VAE signal by individually isolating all variables. For the first time, this paper demonstrates the influence of backfill on leak VAE signal by keeping all other variables consistent. It was found that the backfill type had a strong influence on the frequency and amplitude of leak signals, which is likely to have a significant impact on the accuracy of leak location estimates
Towards meta-interpretive learning of programming language semantics
We introduce a new application for inductive logic programming: learning the
semantics of programming languages from example evaluations. In this short
paper, we explored a simplified task in this domain using the Metagol
meta-interpretive learning system. We highlighted the challenging aspects of
this scenario, including abstracting over function symbols, nonterminating
examples, and learning non-observed predicates, and proposed extensions to
Metagol helpful for overcoming these challenges, which may prove useful in
other domains.Comment: ILP 2019, to appea
Inductive learning spatial attention
This paper investigates the automatic induction of spatial attention
from the visual observation of objects manipulated
on a table top. In this work, space is represented in terms of
a novel observer-object relative reference system, named Local
Cardinal System, defined upon the local neighbourhood
of objects on the table. We present results of applying the
proposed methodology on five distinct scenarios involving
the construction of spatial patterns of coloured blocks
Diversity, urban space and the right to the provincial city
Using three vignettes of the same physical space this article contributes to understanding of how the right to the city is contested in provincial England in the early twenty-first century. Oral history and ethnographic material gathered in Peterborough between 2010 and 2012 are drawn on to shed new light on the politics of diversity and urban space. This highlights the multiple place attachments and trans-spatial practices of all residents, including the white ethnic majority, as well as contrasting forms of active intervention in space with their different temporalities and affective intensities. The article carries its own diversity politics, seeking to reduce the harm done by racism through challenging the normalisation of the idea of a local, indigenous population, left out by multiculturalism. It simultaneously raises critical questions about capitalist regeneration strategies in terms of their impact both on class inequality and on the environment
‘Mapping the Underworld’: recent developments in vibro-acoustic techniques to locate buried infrastructure
Statistical relational learning with soft quantifiers
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results
Rapid preimplantation detection of mutant (shiverer) and normal alleles of the mouse myelin basic protein gene allowing selective implantation and birth of live young.
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