Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 153.68K | Year: 2014
In the age of Big Data, knowledge workers - individuals, companies and organisations whose primary focus is knowledge and information extraction and usage - find it increasingly difficult to search for and identify accurate and relevant information. In the domain of scientific literature and IP search, where the underlying corpora are growing at a huge rate, this is a daunting task and human expertise and involvement remain critical. This project aims to develop a suite of tools that will enable users to search for and identify relevant information within a corpus more efficiently and effectively. The methods developed will deploy new search paradigms together with semantic-based analysis, domain and lexical linguistic ontologies in order to understand the user needs based on the underlying domain of application and subsequently enable accurate information retrieval through enhanced search and cross-reference of information. The project aims to offer tools for sharing of search strategies which will be identified by observing and understanding patterns in users search behaviours.
News Article | February 15, 2017
Wiseguyreports.Com Adds “Healthcare Natural Language Processing -Market Demand, Growth, Opportunities and analysis of Top Key Player Forecast to 2022” To Its Research Database This report studies sales (consumption) of Healthcare Natural Language Processing in United States market, focuses on the top players, with sales, price, revenue and market share for each player, covering Split by product types, with sales, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Split by applications, this report focuses on sales, market share and growth rate of Healthcare Natural Language Processing in each application, can be divided into Application 1 Application 2 United States Healthcare Natural Language Processing Market Report 2017 1 Healthcare Natural Language Processing Overview 1.1 Product Overview and Scope of Healthcare Natural Language Processing 1.2 Classification of Healthcare Natural Language Processing 1.2.1 Type I 1.2.2 Type II 1.3 Application of Healthcare Natural Language Processing 1.3.1 Application 1 1.3.2 Application 2 1.4 United States Market Size Sales (Volume) and Revenue (Value) of Healthcare Natural Language Processing (2012-2022) 1.4.1 United States Healthcare Natural Language Processing Sales and Growth Rate (2012-2022) 1.4.2 United States Healthcare Natural Language Processing Revenue and Growth Rate (2012-2022) 6 United States Healthcare Natural Language Processing Manufacturers Profiles/Analysis 6.1 3M 6.1.1 Company Basic Information, Manufacturing Base and Competitors 6.1.2 Healthcare Natural Language Processing Product Type, Application and Specification 18.104.22.168 Product A 22.214.171.124 Product B 6.1.3 3M Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.1.4 Main Business/Business Overview 6.2 IBM 6.2.2 Healthcare Natural Language Processing Product Type, Application and Specification 126.96.36.199 Product A 188.8.131.52 Product B 6.2.3 IBM Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.2.4 Main Business/Business Overview 6.3 Cerner Corp 6.3.2 Healthcare Natural Language Processing Product Type, Application and Specification 184.108.40.206 Product A 220.127.116.11 Product B 6.3.3 Cerner Corp Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.3.4 Main Business/Business Overview 6.4 Nuance Communication 6.4.2 Healthcare Natural Language Processing Product Type, Application and Specification 18.104.22.168 Product A 22.214.171.124 Product B 6.4.3 Nuance Communication Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.4.4 Main Business/Business Overview 6.5 Microsoft Corp 6.5.2 Healthcare Natural Language Processing Product Type, Application and Specification 126.96.36.199 Product A 188.8.131.52 Product B 6.5.3 Microsoft Corp Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.5.4 Main Business/Business Overview 6.6 Health Fidelity 6.6.2 Healthcare Natural Language Processing Product Type, Application and Specification 184.108.40.206 Product A 220.127.116.11 Product B 6.6.3 Health Fidelity Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.6.4 Main Business/Business Overview 6.7 Apixio 6.7.2 Healthcare Natural Language Processing Product Type, Application and Specification 18.104.22.168 Product A 22.214.171.124 Product B 6.7.3 Apixio Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.7.4 Main Business/Business Overview 6.8 Linguamatics 6.8.2 Healthcare Natural Language Processing Product Type, Application and Specification 126.96.36.199 Product A 188.8.131.52 Product B 6.8.3 Linguamatics Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.8.4 Main Business/Business Overview 6.9 Optum 6.9.2 Healthcare Natural Language Processing Product Type, Application and Specification 184.108.40.206 Product A 220.127.116.11 Product B 6.9.3 Optum Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.9.4 Main Business/Business Overview 6.10 Dolbey Systems 6.10.2 Healthcare Natural Language Processing Product Type, Application and Specification 18.104.22.168 Product A 22.214.171.124 Product B 6.10.3 Dolbey Systems Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.10.4 Main Business/Business Overview For more information, please visit https://www.wiseguyreports.com/sample-request/905558-global-dry-wine-sales-market-report-2017
News Article | November 30, 2016
CAMBRIDGE, England and BOSTON, Nov. 30, 2016 /PRNewswire/ -- Text analytics provider Linguamatics today released the latest version of their award-winning natural language processing (NLP) text mining platform, I2E 5.0. Logo - http://http://photos.prnewswire.com/prnh/20161129/443803LOG...
News Article | February 20, 2017
CAMBRIDGE, United Kingdom and BOSTON, Feb. 20, 2017 /PRNewswire/ -- Clinical NLP provider Linguamatics, and Varian Medical Systems, today announced that Varian will utilize Linguamatics' natural language processing (NLP) technology as part of the data analytics within Varian's 360 Oncology...
News Article | November 8, 2016
— The report "Natural Language Processing (NLP) in Healthcare and Life Sciences Market by Component (Technology and Services), Type (Rule-based, Statistical and Hybrid), Application, Deployment Mode (Cloud and On Premise) and Region - Global Forecast to 2021", Natural Language Processing (NLP) in healthcare and life sciences market to grow from USD 1.03 Billion in 2016 to USD 2.65 Billion by 2021, at a Compound Annual Growth Rate (CAGR) of 20.8%. The NLP in healthcare and life sciences market is growing rapidly because of the huge surge in clinical data, increase in the use of connected devices, and evolving consumer needs. Browse 66 market data tables and 46 figures spread through 132 pages and in-depth TOC on “Natural Language Processing (NLP) in Healthcare and Life Sciences Market" Early buyers will receive 10% customization on this report. “Report generation application to grow at the highest CAGR” The report segments the global market on the basis of applications that include machine translation, automated information extraction, report generation, predictive risk analytics, and others including question answering, dialogue systems, email filtering, spelling correction, and search engine. Report generation is expected to witness the highest growth rate. The report generation application is used to extract required specific information from database, XML, or spreadsheets, and use that information to generate the document as per the requirement of users. Health care organizations and clinics are in constant need for high quality clinical data and this can be achieved by using NLP technology. With the help of distinct automated semantic algorithm, report generation application reorganizes the document as per the human needs. “Statistical NLP expected to have the largest market share in 2016” The report segments the global market by type into rule-based NLP, statistical NLP, and hybrid NLP. Among these, hybrid NLP is expected to dominate the market in term of the market share. The statistical based NLP approach is one of the better approaches to generate results when large amount of data is involved. Due to huge volume availability of text and speech corpora, the statistical-NLP has shown remarkable success in the past few decades. “North America to be the largest revenue generator” The report has been segmented by region into North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is expected to continue being the largest revenue generator region for NLP in healthcare and life sciences vendors for the next five years, followed by Europe. Major initiatives taken for NLP technologies have their origin in this region. Increase in adoption and usage of smartphones and big data are a few of the major drivers of NLP solutions in North America. The market growth in developing regions can be attributed to the enhancements in technology. Major vendors that offer NLP in healthcare and life sciences solutions are 3M (Minnesota), Cerner Corporation (Missouri), IBM Corporation (New York), Microsoft Corporation (Washington), Nuance Communications (Massachusetts), M*Modal (Tennessee), Health Fidelity (California), Dolbey Systems (Ohio), Linguamatics (Cambridge), and Apixio (San Mateo). These vendors have adopted different types of organic and inorganic growth strategies such as new product launches, partnerships & collaborations, and mergers & acquisitions to expand their offerings in the NLP in healthcare and life sciences market. MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model – GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M’s flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical infographics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository. For more information, please visit http://www.marketsandmarkets.com/Market-Reports/healthcare-lifesciences-nlp-market-131821021.html
Agency: European Commission | Branch: FP7 | Program: CSA | Phase: ICT-2007.4.4 | Award Amount: 2.20M | Year: 2009
This proposal defines a support action project that brings together the researchers from international biomedical text-mining groups to address the difficult issue of annotating large text corpora with a large set of semantic types. We propose a collaborative approach to this annotation task in the form of an open challenge to the biomedical text-mining community. The task is the annotation of named entities in a large biomedical corpus, for a variety of semantic categories. The project delivers as outcome a large, collaboratively annotated corpus, marked with the mentions of biomedical entities. The annotated corpus becomes a resource for the community, to be used as a reference for improving text-mining applications. The biomedical text-mining research community has a long tradition of organizing such challenges, as a way of evaluating techniques, sharing technical knowledge, and helping to improve the results from text-mining programs. However, such challenges have typically addressed relatively small corpora in a narrow sub-domain, in part because the evaluation of the results is extremely long and costly. As a result, the generated annotated corpora are too small and are only narrowly annotated to be useful in a variety of text-mining applications. In contrast, we propose to create a broadly-scoped and large annotated corpus by integrating the annotations from different named entity recognition systems. Metadata will also be added to the corpus. The participating systems have different application scopes and annotation strategies, and therefore complement each other. As a consequence, the annotated corpus reflects these different scopes and strategies. A secondary goal of this project is to define a standardized format for representing the annotations contributed by the participants and comparing them effectively. Currently the lack of such a format hinders progress in the evaluation of named entity recognition systems.
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2011.4.1 | Award Amount: 2.31M | Year: 2012
This project will provide multilingual terminologies and semantically annotated multilingual documents, e.g., patent texts, to improve the accessibility of scientific information from multilingual documents. The two SME partners will use these resources to improve the quality and functionality of their product offerings, viz. delivering multilingual search and text mining engines based on multilingual terminologies. Both SMEs will market these solutions to their customer base.The MANTRA project capitalizes on parallel document corpora from which translational correspondences will be computed by the use of different alignment methods. Fortunately, the biomedical domain, the application scenario of MANTRA, offers a rich variety of such parallel corpora. We will exploit these multilingual document sets to harvest terms and concept representations in different languages in order to augment currently available terminological resources such as the Medical Subject Headings (MeSH).The project partners will collaboratively build two types of resources: automatically enhanced multilingual terminologies and semantically annotated multilingual documents. The novelty of the latter resource derives from the fact that we solicit and orchestrate community efforts for building up these annotated resources, a procedure that has already been proven successful for the semantic enrichment of large-scale biomedical document corpora (CALBC project) which was executed by the project partners. The novelty of the first comes from a new combination of existing technologies in the area of statistical machine translation, named entity tagging and terminological resources. We start from statistically aligned, parallel documents on which named entity taggers are run to produce highly diverse semantic (named entity) annotations. These annotations signal concept mentions in the text which can then be linked to corresponding entries in relevant biomedical ontologies (from the UMLS, OBO or BioPortal umbrellas), and, in addition, provide the corresponding concept identifiers. Parallel named entity occurrences lacking links to the chosen ontologies can be considered as putative translation equivalents. Validated putative translation equivalents can then be used to enhance already given monolingual terminological resources. Both types of resources will be made available to the public for translation purposes and for search in and text mining from multilingual documents.
News Article | November 17, 2016
Text analytics can be defined as a process of retrieving information from the available text sources. Text analytics is used for various purposes such as summarization of information, classification, sentimental analysis and data investigation. Text Analytics has become vital for smooth functioning of business across world. Text Analytics software helps its end user to perform data analysis for obtaining useful insights. Text Analytics software’s are capable to process structured as well as unstructured data in a same efficient manner. In text analytics software, natural language processing toolkits are used which are competent enough to overcome any type of language barrier and this toolkit can derive information from any unknown languages. Modern text analytics software offers user friendly interface for better representation and analysis. Globally, need of effective text analytics solution and services is increasing steadily. Factors which are driving the growth of global text analytics service market are growing demand of social media analysis for effective brand building, development of multilingual text analytics to overcome language barriers, increasing concern of financial frauds and growing big data market. On the other hand, factors which are restraining the growth of global text analytics market are lack of awareness among end users about software handling, high deployment cost and compliance issue with present IT infrastructure. However, added advantage of predictive analytics and credibility to analyse big data is expected to create great opportunity for text analytics market in future. Global text analytics market is segmented on the basis of application, organization size, deployment model and verticals. On the basis of application, the global text analytics market can be segmented into, enterprise application, predictive analytics, data analytics application, web-based application, search based applications and others. Predictive analytics segment is expected to be major application segment of global text analytics market during the forecast period On the basis of organization size, the global text analytics market can be segmented into small enterprises, medium enterprises and large enterprises. On the basis of deployment model, the global text analytics market can be segmented into on-premise and cloud deployment. Cloud based deployment model expected to gain significant importance during the period of forecast. However, small enterprises are still very much dependent on on-premise model On the basis of verticals, the global text analytics market can be segmented into banking, financial serves and insurance (BFSI), healthcare & pharmaceuticals, manufacturing, retail & hospitality, telecommunication, consumer packed goods and others On the basis of region, the text analytics market can be segmented into seven regions which includes, North America, Latin America, Western Europe, Asia-Pacific (excluding Japan), Eastern Europe, Japan and Middle East & Africa region. Further the market is sub-segmented as per the major countries of each region in order to provide better regional analysis of the Text Analytics Market. North America region is expected to dominate the global text analytics market during the period of forecast. Western Europe region is expected to be second largest market in terms of revenue during the forecast period. Key players in global text analytics market are IBM Corporation, Microsoft, Hewlett-Packard Development Company, L.P., Attensity Inc., Clarabridge, SAP SE, TIBCO Software Inc., Tableau Software and Oracle among others. Key players are focusing on continuous innovations in the existing text analytics software’s. Hence new product launch is the major development strategy adopted by market players in order to grow in market. In 2014, Linguamatics launched I2E Semantic Enrichment to offer increased return on investment in enterprise search systems. The research report presents a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, and statistically supported and industry-validated market data. It also contains projections using a suitable set of assumptions and methodologies. The research report provides analysis and information according to categories such as application, organization size, deployment model and verticals.
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 269.85K | Year: 2014
CambridgeIP (CIP) the global innovation and intellectual property consultancy, with the University of Cambridge (UoC) and Royal Society of Chemistry (RSC), propose to develop novel touch interfaces to global scientific literature archives, enabling more intuitive search and analysis across multiple devices. With over 1 billion smartphone users now performing traditionally pc-based activites on their phone, new techniques need to be used for big data analysis. This will be achieved by using the latest advances in touch-screen and mobile interfaces, alongside semantic data analysis. Touch interfaces to the semantic elements will create an intuitive, accessible search platform enabling high level analysis and exploration of highly complex and specialist data sectors. Interactive data analytics and higher level data visualisations will be created to help view patterns within the data. The project will improve specialist and non-specialist access to valuable information from global scientific literature, enhancing R&D, education and entepreneurship.
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 164.86K | Year: 2011
The current generation of language processing has had considerable success in extracting useful information from large amounts of unstructured text, whether this is research literature or social media. However, adapting to a new domain is often a laborious process, with respect both to diverse types of data (e.g. newswire vs. patent literature) and to the terminology used in a given domain (e.g. in medical practice vs. pharmaceutical research). Humans can perform these tasks on small data sets, but face a challenge in the face of massively increasing amounts of electronic text. The EVOKES project is exploiting distributional similarity techniques to accelerate key components of customisation - the recognition of concepts, and the creation or adaptation of terminologies that link terms to concepts.