An Architecture for Data and Knowledge Acquisition for the Semantic Web: the AGROVOC Use Case

We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships

Towards large-scale language analysis in the cloud

This paper documents ongoing work within the Norwegian CLARINO project on building a Language Analysis Portal (LAP). The portal will provide an intuitive and easily accessible web interface to a centralized repository of a wide range of language technology tools, all installed on a high-performance computing cluster. Users will be able to compose and run workflows using an easy-to-use graphical interface, with multiple tools and resources chained together in potentially complex pipelines. Although the project aims to reach out to a diverse set of user groups, it particularly will facilitate use of language analysis in the social sciences, humanities, and other fields without strong computational traditions. While the development of the portal is still in its early stages, this paper documents ongoing work towards an already operable pilot in addition to providing an overview of long-term goals and visions. At the core of the current pilot implementation we find Galaxy, a web-based workflow management system initially developed for data-intensive research in genomics and bioinformatics; therefore, an important part of the work on the pilot is to adapt and evaluate Galaxy for the context of a language analysis portal. Emanuele Lapponi, Erik Velldal, Nikolay A. Vazov, Stephan Oepen (2013). Towards Large-Scale Language Analysis in the Cloud, Proceedings of the workshop on Nordic language research infrastructure at NODALIDA 2013, May 22-24, 2013, Oslo, Norway. NEALT Proceedings Series 20 http://www.ep.liu.se/ecp_article/index.en.aspx?issue=089;article=00

TimeML: An ontological mapping onto UIMA Type Systems

We present TeR, an UIMA Type System (Ferrucci and Lally, 2004) for event recognition, for temporal annotation in an Italian corpus. We map each TIMEML category (Pustejovsky et al., 2006) to one or more semantic types as they have been defined in the SIMPLE-CLIPS ontology (Ruimy et al., 2003). This mapping presents some advantages, such as the orthogonal inheritance that an event can acquire when derived from the ontology and a clearer definition of semantic roles when carried out by events. The mapping is implemented by means of a FINITE STATE AUTOMATON which uses semantic information collected from the SIMPLE-CLIPS ontology to analyze natural language texts

Report on the 2015 NSF Workshop on Unified Annotation Tooling

On March 30 & 31, 2015, an international group of twenty-three researchers with expertise in linguistic annotation convened in Sunny Isles Beach, Florida to discuss problems with and potential solutions for the state of linguistic annotation tooling. The participants comprised 14 researchers from the U.S. and 9 from outside the U.S., with 7 countries and 4 continents represented, and hailed from fields and specialties including computational linguistics, artificial intelligence, speech processing, multi-modal data processing, clinical & medical natural language processing, linguistics, documentary linguistics, sign-language linguistics, corpus linguistics, and the digital humanities. The motivating problem of the workshop was the balkanization of annotation tooling, namely, that even though linguistic annotation requires sophisticated tool support to efficiently generate high-quality data, the landscape of tools for the field is fractured, incompatible, inconsistent, and lacks key capabilities. The overall goal of the workshop was to chart the way forward, centering on five key questions: (1) What are the problems with current tool landscape? (2) What are the possible benefits of solving some or all of these problems? (3) What capabilities are most needed? (4) How should we go about implementing these capabilities? And, (5) How should we ensure longevity and sustainability of the solution? I surveyed the participants before their arrival, which provided significant raw material for ideas, and the workshop discussion itself resulted in identification of ten specific classes of problems, five sets of most-needed capabilities. Importantly, we identified annotation project managers in computational linguistics as the key recipients and users of any solution, thereby succinctly addressing questions about the scope and audience of potential solutions. We discussed management and sustainability of potential solutions at length. The participants agreed on sixteen recommendations for future work. This technical report contains a detailed discussion of all these topics, a point-by-point review of the discussion in the workshop as it unfolded, detailed information on the participants and their expertise, and the summarized data from the surveys

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user\u27s comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development

TeXTracT: a Web-based Tool for Building NLP-enabled Applications

Over the last few years, the software industry has showed an increasing interest for applications with Natural Language Processing (NLP) capabilities. Several cloud-based solutions have emerged with the purpose of simplifying and streamlining the integration of NLP techniques via Web services. These NLP techniques cover tasks such as language detection, entity recognition, sentiment analysis, classification, among others. However, the services provided are not always as extensible and configurable as a developer may want, preventing their use in industry-grade developments and limiting their adoption in specialized domains (e.g., for analyzing technical documentation). In this context, we have developed a tool called TeXTracT that is designed to be composable, extensible, configurable and accessible. In our tool, NLP techniques can be accessed independently and orchestrated in a pipeline via RESTful Web services. Moreover, the architecture supports the setup and deployment of NLP techniques on demand. The NLP infrastructure is built upon the UIMA framework, which defines communication protocols and uniform service interfaces for text analysis modules. TeXTracT has been evaluated in two case-studies to assess its pros and cons.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Natural Language Processing: Integration of Automatic and Manual Analysis

There is a current trend to combine natural language analysis with research questions from the humanities. This requires an integration of automatic analysis with manual analysis, e.g. to develop a theory behind the analysis, to test the theory against a corpus, to generate training data for automatic analysis based on machine learning algorithms, and to evaluate the quality of the results from automatic analysis. Manual analysis is traditionally the domain of linguists, philosophers, and researchers from other humanities disciplines, who are often not expert programmers. Automatic analysis, on the other hand, is traditionally done by expert programmers, such as computer scientists and more recently computational linguists. It is important to bring these communities, their tools, and data closer together, to produce analysis of a higher quality with less effort. However, promising cooperations involving manual and automatic analysis, e.g. for the purpose of analyzing a large corpus, are hindered by many problems: - No comprehensive set of interoperable automatic analysis components is available. - Assembling automatic analysis components into workflows is too complex. - Automatic analysis tools, exploration tools, and annotation editors are not interoperable. - Workflows are not portable between computers. - Workflows are not easily deployable to a compute cluster. - There are no adequate tools for the selective annotation of large corpora. - In automatic analysis, annotation type systems are predefined, but manual annotation requires customizability. - Implementing new interoperable automatic analysis components is too complex. - Workflows and components are not sufficiently debuggable and refactorable. - Workflows that change dynamically via parametrization are not readily supported. - The user has no control over workflows that rely on expert skills from a different domain, undocumented knowledge, or third-party infrastructures, e.g. web services. In cooperation with researchers from the humanities, we develop innovative technical solutions and designs to facilitate the use of automatic analysis and to promote the integration of manual and automatic analysis. To address these issues, we set foundations in four areas: - Usability is improved by reducing the complexity of the APIs for building workflows and creating custom components, improving the handling of resources required by such components, and setting up auto-configuration mechanisms. - Reproducibility is improved through a concept for self-contained, portable analysis components and workflows combined with a declarative modeling approach for dynamic parametrized workflows, that facilitates avoiding unnecessary auxiliary manual steps in automatic workflows. - Flexibility is achieved by providing an extensive collection of interoperable automatic analysis components. We also compare annotation type systems used by different automatic analysis components to locate design patterns that allow for customization when used in manual analysis tasks. - Interactivity is achieved through a novel "annotation-by-query" process combining corpus search with annotation in a multi-user scenario. The process is supported by a web-based tool. We demonstrate the adequacy of our concepts through examples which represent whole classes of research problems. Additionally, we integrated all our concepts into existing open-source projects, or we implemented and published them within new open-source projects

TeXTracT: a Web-based Tool for Building NLP-enabled Applications

Over the last few years, the software industry has showed an increasing interest for applications with Natural Language Processing (NLP) capabilities. Several cloud-based solutions have emerged with the purpose of simplifying and streamlining the integration of NLP techniques via Web services. These NLP techniques cover tasks such as language detection, entity recognition, sentiment analysis, classification, among others. However, the services provided are not always as extensible and configurable as a developer may want, preventing their use in industry-grade developments and limiting their adoption in specialized domains (e.g., for analyzing technical documentation). In this context, we have developed a tool called TeXTracT that is designed to be composable, extensible, configurable and accessible. In our tool, NLP techniques can be accessed independently and orchestrated in a pipeline via RESTful Web services. Moreover, the architecture supports the setup and deployment of NLP techniques on demand. The NLP infrastructure is built upon the UIMA framework, which defines communication protocols and uniform service interfaces for text analysis modules. TeXTracT has been evaluated in two case-studies to assess its pros and cons.Sociedad Argentina de Informática e Investigación Operativa (SADIO