Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.Comment: EMNLP 2017 (long paper

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation

Value driven conceptual design of Unmanned Air System for defence applications

The work presented concerns the development of a value driven conceptual design assessment framework for a small Unmanned Air System (UAS) to be utilized in a defence application. In the field of Multi-Disciplinary Design Optimization, most recent systematic search has been devoted to fixed topology parametric geometries, pertaining to a single concept, with very little stress put on the optimization of variable topologies describing alternative design concepts. The search is conducted in a highly novel manner, generating a broad range of combinations of UAS configurations and geometries by systematically searching alternative concepts and design configurations through the parameterization of the aircraft geometric topologies. Moreover, the “value” of proposed solutions is assessed in an objective way both from performance and economic perspectives, while the optimal solution is identified after relaxing all of the design constraints as advocated by value driven design philosophy. During the multi-criteria decision analysis, the quantification/conversion of the linguistic preferences of the user between the various attributes to numerical values has disclosed some deficiencies introduced by the unjustifiable numerical scales used in the Analytic Hierarchy Process (AHP). This problem is resolved by a novel value model synthesizing the AHP assessment methodologies with multi-attribute value-focused analysis

A Novel Approach to Ontology Management

The term ontology is defined as the explicit specification of a conceptualization. While much of the prior research has focused on technical aspects of ontology management, little attention has been paid to the investigation of issues that limit the widespread use of ontologies and the evaluation of the effectiveness of ontologies in improving task performance. This dissertation addresses this void through the development of approaches to ontology creation, refinement, and evaluation. This study follows a multi-paper model focusing on ontology creation, refinement, and its evaluation. The first study develops and evaluates a method for ontology creation using knowledge available on the Web. The second study develops a methodology for ontology refinement through pruning and empirically evaluates the effectiveness of this method. The third study investigates the impact of an ontology in use case modeling, which is a complex, knowledge intensive organizational task in the context of IS development. The three studies follow the design science research approach, and each builds and evaluates IT artifacts. These studies contribute to knowledge by developing solutions to three important issues in the effective development and use of ontologies

The Intensive Cognitive-Communication Rehabilitation Program for young adults with acquired brain injury

PURPOSE: This study investigated the effects of an intensive cognitive-communication rehabilitation (ICCR) program for young individuals with chronic acquired brain injury. METHOD: ICCR included classroom lectures; metacognitive instruction, modeling, and application; technology skills training; and individual cognitive-linguistic therapy. Four individuals participated in the intensive program (6 hr with 1-hr lunch break × 4 days × 12 weeks of treatment): 3 participants completed 3 consecutive semesters, and 1 participant completed 1 semester. Two controls did not receive treatment and completed assessments before and after the 12-week treatment interval only. RESULTS: All 4 experimental participants demonstrated significant improvements on at least 1 standardized cognitive-linguistic measure, whereas controls did not. Furthermore, time point significantly predicted participants' scores on 2 of the 4 standardized outcome measures, indicating that as duration in ICCR increased, scores also increased. Participants who completed multiple semesters of ICCR also improved in their therapy and personal goals, classroom behavior, life participation, and quality of life. CONCLUSION: After ICCR, participants showed gains in their cognitive-linguistic functioning, classroom participation, and individual therapy. They also demonstrated improvements outside the classroom and in their overall well-being. There is a gap between the large population of young adults with acquired brain injury who wish to return to higher education and a lack of rehabilitation programs supporting reentry into academic environments; ICCR is a first step in reducing that gap.T32 DC013017 - NIDCD NIH HHSAccepted manuscrip