Unsupervised Entailment Detection between Dependency Graph Fragments

Entailment detection systems are generally designed to work either on single words, relations or full sentences. We propose a new task – detecting entailment between dependency graph fragments of any type – which relaxes these restrictions and leads to much wider entailment discovery. An unsupervised framework is described that uses intrinsic similarity, multi-level extrinsic similarity and the detection of negation and hedged language to assign a confidence score to entailment relations between two fragments. The final system achieves 84.1% average precision on a data set of entailment examples from the biomedical domain

Parser lexicalisation through self-learning

We describe a new self-learning framework for parser lexicalisation that requires only a plain-text corpus of in-domain text. The method first creates augmented versions of dependency graphs by applying a series of modifications designed to directly capture higherorder lexical path dependencies. Scores are assigned to each edge in the graph using statistics from an automatically parsed background corpus. As bilexical dependencies are sparse, a novel directed distributional word similarity measure is used to smooth edge score estimates. Edge scores are then combined into graph scores and used for reranking the topn analyses found by the unlexicalised parser. The approach achieves significant improvements on WSJ and biomedical text over the unlexicalised baseline parser, which is originally trained on a subset of the Brown corpus

Constrained multi-task learning for automated essay scoring

Supervised machine learning models for automated essay scoring (AES) usually require substantial task-specific training data in order to make accurate predictions for a particular writing task. This limitation hinders their utility, and consequently their deployment in real-world settings. In this paper, we overcome this shortcoming using a constrained multi-task pairwisepreference learning approach that enables the data from multiple tasks to be combined effectively. Furthermore, contrary to some recent research, we show that high performance AES systems can be built with little or no task-specific training data. We perform a detailed study of our approach on a publicly available dataset in scenarios where we have varying amounts of task-specific training data and in scenarios where the number of tasks increases.This is the author accepted manuscript. The final version is available from Association for Computational Linguistics at http://acl2016.org/index.php?article_id=71

Automatic extraction of learner errors in ESL sentences using linguistically enhanced alignments

We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Association for Computational Linguistics

Mortification and Apodiorizo: Re-framing Apologia

Image restoration strategies and apologia have been used for years to explain how speakers engage in verbal self-defense. Kategoria has expanded our understanding of apologia when rhetors counter with an accusation to explain or justify their behavior. In recent years, however, a new tactic has emerged in apologia in which speakers admit to the transgression but then accuse the media of invading their privacy by stalking their families. Following the accusation, these speakers draw a boundary with the media and the audience regarding what the media can and cannot do. This strategy is unique because the rhetor does not attempt to create a scapegoat. The rhetor takes full responsibility for the transgression, sometimes even taunting the media to “come after me,” but then demands the media leave their family alone. This strategy of bringing a charge and drawing a boundary is absent in current image restoration literature. This essay will identify this new rhetorical posture as apodiorizo

London Creative and Digital Fusion

date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000The London Creative and Digital Fusion programme of interactive, tailored and in-depth support was designed to support the UK capital’s creative and digital companies to collaborate, innovate and grow. London is a globally recognised hub for technology, design and creative genius. While many cities around the world can claim to be hubs for technology entrepreneurship, London’s distinctive potential lies in the successful fusion of world-leading technology with world-leading design and creativity. As innovation thrives at the edge, where better to innovate than across the boundaries of these two clusters and cultures? This booklet tells the story of Fusion’s innovation journey, its partners and its unique business support. Most importantly of all it tells stories of companies that, having worked with London Fusion, have innovated and grown. We hope that it will inspire others to follow and build on our beginnings.European Regional Development Fund 2007-13

Phase transition and hysteresis in scale-free network traffic

We model information traffic on scale-free networks by introducing the node queue length L proportional to the node degree and its delivering ability C proportional to L. The simulation gives the overall capacity of the traffic system, which is quantified by a phase transition from free flow to congestion. It is found that the maximal capacity of the system results from the case of the local routing coefficient \phi slightly larger than zero, and we provide an analysis for the optimal value of \phi. In addition, we report for the first time the fundamental diagram of flow against density, in which hysteresis is found, and thus we can classify the traffic flow with four states: free flow, saturated flow, bistable, and jammed.Comment: 5 pages, 4 figure

Detecting learner errors in the choice of content words using compositional distributional semantics

We describe a novel approach to error detection in adjective-noun combinations. We present and release a new dataset of annotated errors where the examples are extracted from learner texts and annotated with error types. We show how compositional distributional semantic approaches can be applied to discriminate between correct and incorrect word combinations from learner data. Finally, we show how the output of the compositional distributional semantic models can be used as features in a classifier yielding good precision and accuracy.We are grateful to Cambridge English Language Assessment and Cambridge University Press for supporting this research and for granting us access to the CLC for research purposes

Combining manual rules and supervised learning for hedge cue and scope detection

Hedge cues were detected using a supervised Conditional Random Field (CRF) classifier exploiting features from the RASP parser. The CRF’s predictions were filtered using known cues and unseen instances were removed, increasing precision while retaining recall. Rules for scope detection, based on the grammatical relations of the sentence and the part-of-speech tag of the cue, were manually developed. However, another supervised CRF classifier was used to refine these predictions. As a final step, scopes were constructed from the classifier output using a small set of post-processing rules. Development of the system revealed a number of issues with the annotation scheme adopted by the organisers