ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies

We introduce EXTASEM!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect thousands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manuallyvalidated data. Finally, we introduce a graph induction procedure which constructs a full-fledged taxonomy where each edge is weighted according to its domain pertinence. EXTASEM! achieves state-of-the-art results in the following taxonomy evaluation experiments: (1) Hypernym discovery, (2) Reconstructing gold standard taxonomies, and (3) Taxonomy quality according to structural measures. We release weighted taxonomies for six domains for the use and scrutiny of the communit

National benchmark for green infrastructure: A feasibility study

This research examines whether a market exists for a national benchmark for green infrastructure (GI) in England. It is funded through a Natural Environment Research Council Innovation Fund (Grant Reference: NE/N016971/1). This builds on a Knowledge Transfer Partnership between the University of the West of England (UWE) and Gloucestershire Wildlife Trust, a project which includes the development of a local benchmark for Gloucestershire and the West of England and which focusses, naturally, on local priorities.This project sought to answer three main questions:•What is the demand for a GI benchmark in the built environment sector?•What types of GI and corresponding ecosystem services should the benchmark include?•What is the most appropriate model to ensure the long-term success of the benchmark?First, a desktop review of relevant assessment systems was conducted to examine a) if, and how, GI is incorporated into such systems and b) their overall operation to understand current practice within the built environment sector. The desktop review included 22 assessment systems, including benchmarks for green developments (building and community-scale) and other infrastructure, and audits, awards, guidance and tool kits that related more specifically to GI, green space or biodiversity.Second, five Expert Symposia were held to test the findings of the review as well as the initial work completed in the KTP on experts from the built environment and GI professions. Thus, the five symposia were co-hosted by the Royal Institution of Chartered Surveyors (RICS), Landscape Institute, Royal Town Planning Institute (RTPI), The Royal Society of Wildlife Trusts (RSWT), and Town and Country Planning Association (TCPA). Whilst the first three of these were quite profession specific, the latter two included participants from a broader range of backgrounds. A total of 55 experts participated in the symposia

Landscape-scale approaches for integrated natural resource management in tropical forest landscapes

Integrated natural resource management (INRM) helps resource users, managers, and others to manage resources sustainably by considering, reconciling, and synergizing their various interests and activities. Although many social and environmental problems have to be tackled at a range of scales to be resolved successfully, INRM has particular relevance at the landscape level at which the interests of local people first intersect those of the outside world. We propose eight guidelines for building successful INRM programs: focus on multiscale analysis and intervention; develop partnerships and engage in action research; facilitate change rather than dictating it; promote visioning and the development of scenarios; recognize the importance of local knowledge; foster social learning and adaptive management; concentrate on both people and their natural resources, including biodiversity; and embrace complexity. Reviewing these guidelines in the light of experiences from three separate studies shows that most are being done, though more as a product of happenstance than design. The guidelines form a mutually reinforcing framework for building INRM, primarily through empowering local stakeholders to be more articulate advocates and active participants in their own development and conservation efforts

Technical challenges of providing record linkage services for research

Background: Record linkage techniques are widely used to enable health researchers to gain event based longitudinal information for entire populations. The task of record linkage is increasingly being undertaken by specialised linkage units (SLUs). In addition to the complexity of undertaking probabilistic record linkage, these units face additional technical challenges in providing record linkage ‘as a service’ for research. The extent of this functionality, and approaches to solving these issues, has had little focus in the record linkage literature. Few, if any, of the record linkage packages or systems currently used by SLUs include the full range of functions required. Methods: This paper identifies and discusses some of the functions that are required or undertaken by SLUs in the provision of record linkage services. These include managing routine, on-going linkage; storing and handling changing data; handling different linkage scenarios; accommodating ever increasing datasets. Automated linkage processes are one way of ensuring consistency of results and scalability of service. Results: Alternative solutions to some of these challenges are presented. By maintaining a full history of links, and storing pairwise information, many of the challenges around handling ‘open’ records, and providing automated managed extractions are solved. A number of these solutions were implemented as part of the development of the National Linkage System (NLS) by the Centre for Data Linkage (part of the Population Health Research Network) in Australia.Conclusions: The demand for, and complexity of, linkage services are growing. This presents as a challenge to SLUs as they seek to service the varying needs of dozens of research projects annually. Linkage units need to be both flexible and scalable to meet this demand. It is hoped the solutions presented here can help mitigate these difficulties

Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models

In this work, we propose a technique to transfer speech recognition capabilities from audio speech recognition systems to visual speech recognizers, where our goal is to utilize audio data during lipreading model training. Impressive progress in the domain of speech recognition has been exhibited by audio and audio-visual systems. Nevertheless, there is still much to be explored with regards to visual speech recognition systems due to the visual ambiguity of some phonemes. To this end, the development of visual speech recognition models is crucial given the instability of audio models. The main contributions of this work are i) building on recent state-of-the-art word-based lipreading models by integrating sequence-level and frame-level Knowledge Distillation (KD) to their systems; ii) leveraging audio data during training visual models, a feat which has not been utilized in prior word-based work; iii) proposing the Gaussian-shaped averaging in frame-level KD, as an efficient technique that aids the model in distilling knowledge at the sequence model encoder. This work proposes a novel and competitive architecture for lip-reading, as we demonstrate a noticeable improvement in performance, setting a new benchmark equals to 88.64% on the LRW dataset.Comment: arXiv admin note: text overlap with arXiv:2108.0354

Ontology Based Semantic Web Information Retrieval Enhancing Search Significance

The web contain huge amount of structured as well as unstructured data/information. This varying nature of data may yield a retrieval response that is expected to contain relevant response that is expected to contain relevant as well as irrelevant data while directing search. In order to filter out irrelevance in the search result, numerous methodologies have been used to extract more and more relevant search responses in retrieval. This work has adopted semantic search dealing directly with the knowledge base. The approach incorporates Query pattern evolution and semantic keyword matching with final detail to enhance significance of relevant data retrieval. The proposed method is implemented in open source computing tool environment and the result obtained thereof are compared with that of earlier used methodologies

Continual Event Extraction with Semantic Confusion Rectification

We study continual event extraction, which aims to extract incessantly emerging event information while avoiding forgetting. We observe that the semantic confusion on event types stems from the annotations of the same text being updated over time. The imbalance between event types even aggravates this issue. This paper proposes a novel continual event extraction model with semantic confusion rectification. We mark pseudo labels for each sentence to alleviate semantic confusion. We transfer pivotal knowledge between current and previous models to enhance the understanding of event types. Moreover, we encourage the model to focus on the semantics of long-tailed event types by leveraging other associated types. Experimental results show that our model outperforms state-of-the-art baselines and is proficient in imbalanced datasets.Comment: Accepted in the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023