1,083 research outputs found

    Serpentuator Patent

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    Internal and external serpentine devices for performing physical operations around orbital space station

    Interactive specification of data displays

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    On-line graphical language for computer data displa

    Annotating textual and speech data in Maltese

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    The present document has been compiled in response to the call for contributions issued by the International Standards Organisation (ISO TC37/SC4 N047) towards the adoption of a morphosyntactic annotation framework. The document aims to contribute samples at the following levels, where the object language is Maltese: a. Tagging: Specifically, part of speech tagging. A tagset for Maltese is included in §3. In addition, a number of problems that arise in relation to the morphosyntactic annotation of Maltese textual documents are described and exemplified, as are current solutions where available, in §2. b. Annotation of transcribed speech. A small set of transcribed utterances are provided, on the basis of which some issues in their annotation are pointed out. Our aim in the compilation of this document has been primarily to draw attention to linguistic phenomena that should be accounted for in a broad-coverage annotation scheme which aims to include the greatest possible number of languages.peer-reviewe

    Information extraction

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    In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates

    When does aggregating multiple skills with multi-task learning work? A case study in financial NLP

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    Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks

    A Chinese Dependency Syntax for Treebanking

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    PACLIC 20 / Wuhan, China / 1-3 November, 200

    Methods of Russian Patent Analysis

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    The article presents a method for extracting predicate-argument constructions characterizing the composition of the structural elements of the inventions and the relationships between them. The extracted structures are converted into a domain ontology and used in prior art patent search and information support of automated invention. The analysis of existing natural language processing (NLP) tools in relation to the processing of Russian-language patents has been carried out. A new method for extracting structured data from patents has been proposed taking into account the specificity of the text of patents and is based on the shallow parsing and segmentation of sentences. The value of the F1 metric for a rigorous estimate of data extraction is 63% and for a lax estimate is 79%. The results obtained suggest that the proposed method is promising

    JACY - a grammar for annotating syntax, semantics and pragmatics of written and spoken japanese for NLP application purposes

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    In this text, we describe the development of a broad coverage grammar for Japanese that has been built for and used in different application contexts. The grammar is based on work done in the Verbmobil project (Siegel 2000) on machine translation of spoken dialogues in the domain of travel planning. The second application for JACY was the automatic email response task. Grammar development was described in Oepen et al. (2002a). Third, it was applied to the task of understanding material on mobile phones available on the internet, while embedded in the project DeepThought (Callmeier et al. 2004, Uszkoreit et al. 2004). Currently, it is being used for treebanking and ontology extraction from dictionary definition sentences by the Japanese company NTT (Bond et al. 2004)
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