CoSimLex : A Resource for Evaluating Graded Word Similarity in Context

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.Peer reviewe

CoSimLex: A Resource for Evaluating Graded Word Similarity in Context

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far

Silver nanoparticle assemblies supported on glassy-carbon electrodes for the electro-analytical detection of hydrogen peroxide.

Electrochemical detection of hydrogen peroxide using an edge-plane pyrolytic-graphite electrode (EPPG), a glassy carbon (GC) electrode, and a silver nanoparticle-modified GC electrode is reported. It is shown, in phosphate buffer (0.05 mol L(-1), pH 7.4), that hydrogen peroxide cannot be detected directly on either the EPPG or GC electrodes. However, reduction can be facilitated by modification of the glassy-carbon surface with nanosized silver assemblies. The optimum conditions for modification of the GC electrode with silver nanoparticles were found to be deposition for 1 min at -0.5 V vs. Ag from 5 mmol L(-1) AgNO3/0.1 mol L(-1) TBAP/MeCN, followed by stripping for 2 min at +0.5 V vs. Ag in the same solution. A wave, due to the reduction of hydrogen peroxide on the silver nanoparticles is observed at -0.68 V vs. SCE. The limit of detection for this modified nanosilver electrode was 2.0 x 10(-6) mol L(-1) for hydrogen peroxide in phosphate buffer (0.05 mol L(-1), pH 7.4) with a sensitivity which is five times higher than that observed at a silver macro-electrode. Also observed is a shoulder on the voltammetric wave corresponding to the reduction of oxygen, which is produced by silver-catalysed chemical decomposition of hydrogen peroxide to water and oxygen then oxygen reduction at the surface of the glassy-carbon electrode