SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER

Simple Recurrent Networks (SRNs) have been widely used in natural language tasks. SARDSRN extends the SRN by explicitly representing the input sequence in a SARDNET self-organizing map. The distributed SRN component leads to good generalization and robust cognitive properties, whereas the SARDNET map provides exact representations of the sentence constituents. This combination allows SARDSRN to learn to parse sentences with more complicated structure than can the SRN alone, and suggests that the approach could scale up to realistic natural language

A look at the other 90 per cent: Investigating British Sign Language vocabulary knowledge in deaf children from different language learning backgrounds

In this study we present new data on deaf children's receptive and expressive vocabulary knowledge in British Sign Language (BSL) from a sample consisting of children with deaf parents, children with hearing parents, and children with additional needs. Their performance on three BSL vocabulary tasks was compared with (previously reported findings from) a sample of deaf fluent signers. We use these data to assess the effects of some key demographic/ child variables on deaf signing children's vocabulary and discuss findings in the relation to the meaning of 'normative' data and samples for this heterogeneous population. Findings show no effect of the presence of additional disabilities on participants' scores for any of the three tasks. As expected, chronological age is the most significant factor in performance on all vocabulary tasks while the number of deaf relatives only becomes statistically significant for the form recall task. This study contributes to the field of sign language assessment by seeking to identify key variables in heterogeneity and how these variables affect signed vocabulary acquisition with the long-term objective of informing intervention

Sentence Repetition in Deaf Children with Specific Language Impairment in British Sign Language

Children with specific language impairment (SLI) perform poorly on sentence repetition tasks across different spoken languages, but until now, this methodology has not been investigated in children who have SLI in a signed language. Users of a natural sign language encode different sentence meanings through their choice of signs and by altering the sequence and inflections of these signs. Grammatical information is expressed through movement and configurational changes of the hands and face. The visual modality thus influences how grammatical morphology and syntax are instantiated. How would language impairment impact on the acquisition of these types of linguistic devices in child signers? We investigated sentence repetition skills in a group of 11 deaf children who display SLI in British Sign Language (BSL) and 11 deaf controls with no language impairment who were matched for age and years of BSL exposure. The SLI group was significantly less accurate on an overall accuracy score, and they repeated lexical items, overall sentence meaning, sign order, facial expressions, and verb morphological structures significantly less accurately than controls. This pattern of language deficits is consistent with the characterization of SLI in spoken languages even though expression is in a different modality. We conclude that explanations of SLI, and of poor sentence repetition by children with this disorder, must be able to account for both the spoken and signed modalities

Management of intra-abdominal infections : recommendations by the WSES 2016 consensus conference

This paper reports on the consensus conference on the management of intra-abdominal infections (IAIs) which was held on July 23, 2016, in Dublin, Ireland, as a part of the annual World Society of Emergency Surgery (WSES) meeting. This document covers all aspects of the management of IAIs. The Grading of Recommendations Assessment, Development and Evaluation recommendation is used, and this document represents the executive summary of the consensus conference findings.Peer reviewe

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Lexical disambiguation based on distributed representations of context frequency

A model for lexical disambiguation is presented that is based on combining the frequencies of past contexts of ambiguous words. The frequencies are encoded in the word representations and de ne the words ' semantics. A Simple Recurrent Network (SRN) parser combines the context frequencies one word at a time, always producing the most likely interpretation of the current sentence at its output. This disambiguation process is most striking when the interpretation involves semantic ipping, that is, an alternation between two opposing meanings as more words are read in. The sense of throwing a ball alternates between dance and baseball as indicators such as the agent, location, and recipient are input. The SRN parser demonstrates how the context frequencies are dynamically combined to determine the interpretation of such sentences. We hypothesize that several other aspects of ambiguity resolution are based on similar mechanisms, and can be naturally approached from the distributed connectionist viewpoint