E-Commerce Applications Ranking

The paper presents the cycle of development of e-Commerce applications. The e-commerce applications are analyzed being considered to be a subject for complex evaluations. A set of criteria and factors are presented being considered relevant for e-commerce applications used in complex assessments. A ranking algorithm is proposed based on the AHP (Analytic Hierarchy Process) process, which was implemented and tested with online application IAID. The objective of this paper is to build, implement and test this algorithm with the online application IAID.E-Commerce, Hierarchy, Evaluation Criteria, Analyses, Ranking Algorithm

HUMAN RESOURCES MANAGEMENT STRATEGYADDRESSED IN RESEARCH PROJECTS

Research entities can achieve sustainable competitive advantages, exercised by strategic operational management of their human resources. But conditions are still unclear: how employees of an eligible research entity can benefit from a strategic human resource management (SHRM) so that they make performances in research - development and innovation, knowing that this area is one with its own status. An important role in the success of national and international research projects plays the human resource management strategy, addressed by the project manager or by the entity that coordinates the project, such as: the relational framework, individual approach, functional factors and organizational level which may influence the implementation of that research projects and which are analyzed in this paperStrategic human resources management, project management, researcher, conflict management.

A Crowdsourced Frame Disambiguation Corpus with Ambiguity

We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201

Capturing Ambiguity in Crowdsourcing Frame Disambiguation

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence. Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.Comment: in publication at the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 201