1. Articles in category: NER

    73-96 of 371 « 1 2 3 4 5 6 7 ... 14 15 16 »
    1. Towards comprehensive syntactic and semantic annotations of the clinical narrative.

      Related Articles Towards comprehensive syntactic and semantic annotations of the clinical narrative. J Am Med Inform Assoc. 2013 Jan 25; Authors: Albright D, Lanfranchi A, Fredriksen A, Styler WF, Warner C, Hwang JD, Choi JD, Dligach D, Nielsen RD, Martin J, Ward W, Palmer M, Savova GK Abstract OBJECTIVE: To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. METHODS: Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for ...
      Read Full Article
      Mentions: Treebank Lee V Palmer
    2. A supervised framework for resolving coreference in clinical records.

      Related Articles A supervised framework for resolving coreference in clinical records. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):875-82 Authors: Rink B, Roberts K, Harabagiu SM Abstract OBJECTIVE: A method for the automatic resolution of coreference between medical concepts in clinical records. MATERIALS AND METHODS: A multiple pass sieve approach utilizing support vector machines (SVMs) at each pass was used to resolve coreference. Information such as lexical similarity, recency of a concept mention, synonymy based on Wikipedia redirects, and local lexical context were used to inform the method. Results were evaluated using an unweighted average of MUC, CEAF ...
      Read Full Article
    3. Active learning for clinical text classification: is it better than random sampling?

      Related Articles Active learning for clinical text classification: is it better than random sampling? J Am Med Inform Assoc. 2012 Sep-Oct;19(5):809-16 Authors: Figueroa RL, Zeng-Treitler Q, Ngo LH, Goryachev S, Wiechmann EP Abstract OBJECTIVE: This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. DESIGN: Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We ...
      Read Full Article
    4. Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.

      Related Articles Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):867-74 Authors: Jonnalagadda SR, Li D, Sohn S, Wu ST, Wagholikar K, Torii M, Liu H Abstract OBJECTIVE: This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity. MATERIALS AND METHODS: The task organizers provided progress notes and discharge summaries that were annotated with the markables ...
      Read Full Article
    5. Automatic discourse connective detection in biomedical text.

      Related Articles Automatic discourse connective detection in biomedical text. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):800-8 Authors: Ramesh BP, Prasad R, Miller T, Harrington B, Yu H Abstract OBJECTIVE: Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. MATERIALS AND ...
      Read Full Article
    6. Named entity recognition of follow-up and time information in 20,000 radiology reports.

      Related Articles Named entity recognition of follow-up and time information in 20,000 radiology reports. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):792-9 Authors: Xu Y, Tsujii J, Chang EI Abstract OBJECTIVE: To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion. METHODS: A novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and ...
      Read Full Article
      Mentions: Beijing China Medline
    7. Extracting Semantic Lexicons from Discharge Summaries using Machine Learning and the C-Value Method.

      Extracting Semantic Lexicons from Discharge Summaries using Machine Learning and the C-Value Method. AMIA Annu Symp Proc. 2012;2012:409-16 Authors: Jiang M, Denny JC, Tang B, Cao H, Xu H Abstract Semantic lexicons that link words and phrases to specific semantic types such as diseases are valuable assets for clinical natural language processing (NLP) systems. Although terminological terms with predefined semantic types can be generated easily from existing knowledge bases such as the Unified Medical Language Systems (UMLS), they are often limited and do not have good coverage for narrative clinical text. In this study, we developed a method ...
      Read Full Article
    8. Mining the pharmacogenomics literature--a survey of the state of the art.

      Related Articles Mining the pharmacogenomics literature--a survey of the state of the art. Brief Bioinform. 2012 Jul;13(4):460-94 Authors: Hahn U, Cohen KB, Garten Y, Shah NH Abstract This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant ...
      Read Full Article
      Mentions: Medline Lee V Shah
    9. Academic research bridges the gap in Arabic language in multiple applications

      Academic research bridges the gap in Arabic language in multiple applications
      * United Arab Emirates: Sunday, December 09 - 2012 at 12:41 * PRESS RELEASE * * * * * * * * Related content The open day event, organized by Abu Dhabi Education Council at Al-Forsan International Sports Resort to commemorate the UAE's 41st National Day. 41st UAE National Day celebrations witnessed... » - Seminal fashion designer Elie Saab and the Lebanese American University partner. LAU and designer Elie Saab partner
      Read Full Article
    10. Recognition of characters and their significance within written works

      Named entity recognition is applied to identify text strings corresponding to character identities in a written work. The textual strings are grouped according to character identity and, from each group, a primary name is selected. A significance is calculated for each of the character identities. The character identities including the primary names are presented in a catalog based on the calculated significance. In some embodiments, character identity identification results are refined by allowing users to vote regarding the significance of the character identities and by granting more weight to the votes of users with a close relationship to the written ...
      Read Full Article
    11. Ripple Down Rules for Vietnamese Named Entity Recognition

      One of the biggest problems with rule based systems is how to avoid the conflict between rules when a new rule is added. Ripple Down Rules (RDR) is considered a good systematic approach to address this for classification problems. In this paper, we present a system using RDR to build the set of rules for Vietnamese Named Entity Recognition which is important for many natural language processing tasks. Experimental results on comparing the proposed approach with a standard method where rules are added in an ad-hoc manner prove to be very promising. Content Type Book ChapterPages 354-363DOI 10.1007/978-3-642-34630-9_37Authors ...
      Read Full Article
    12. A Hybrid Approach of Pattern Extraction and Semi-supervised Learning for Vietnamese Named Entity Recognition

      Requiring a large hand-annotated corpus in supervised learning of contemporary Vietnamese Named Entity Recognition researches is challenging. We therefore propose a hybrid approach of pattern extraction and semi-supervised learning. Applied rule-based method helps generating patterns automatically. Part-of-speech tagger, lexical diversity and chunking are explored to define rules in pattern extractions which are used for identifying potential named entities. Semi-supervised learning trains a small amount of seed named entities to categorize named entities in extracted patterns. In experiments, our approach shows good increasing the system accuracy with others in Vietnamese. Content Type Book ChapterPages 83-93DOI 10.1007/978-3-642-34630-9_9Authors Duc-Thuan Vo, Natural ...
      Read Full Article
    13. Two tiered architecture of named entity recognition engine

      A system (and a method) is disclosed to extract entity values from texts. The system receives, at a first tier entity recognition engine, an input data string having a plurality of entities. The first tier entity recognition engine marks entities of the plurality of entities that are regular expression and transmits the input data stream with the marked entities to a second tier entity recognition engine. The second tier entity recognition engine receives the input data stream and identifies unmarked entities in the input data stream received at the second tier entity recognition engine. The second tier entity recognition engine ...
      Read Full Article
    14. Supervised segmentation of phenotype descriptions for the human skeletal phenome using hybrid methods

      Abstract Background  Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. In order to fully capture the intrinsic value and knowledge expressed within them, we need to take advantage of their inner structure, which implicitly combines qualities and anatomical entities. The first step in this process is the segmentation of the phenotype descriptions into their atomic elements. Results  We present a two-phase hybrid segmentation method that combines a series individual classifiers using different aggregation schemes (set operations and simple majority voting). The approach is tested on ...
      Read Full Article
    15. New progress in geometric computing for image and video processing

      Abstract  In recent years, geometry-based image and video processing methods have aroused significant interest. This paper considers progress from four aspects: geometric characteristics and shape, geometric transformations, embedded geometric structure, and differential geometry methods. Current research trends are also pointed out. Content Type Journal ArticleCategory Review ArticlePages 1-7DOI 10.1007/s11704-012-2910-4Authors Jinjiang Li, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084 ChinaHanyi Ge, Science and Technology on Complex Systems Simulation Laboratory, Beijing, 100101 China Journal Frontiers of Computer ScienceOnline ISSN 2095-2236Print ISSN 2095-2228
      Read Full Article
    16. Extraction and Evaluation of Candidate Named Entities in Search Engine Queries

      Named Entity Recognition (NER) has recently been applied to search queries, in order to better understand their semantics. We present a novel method for detecting candidate named entities (NEs) using grammar annotation and query segmentation with the aid of top-n snippets from search engine results, and a web n-gram model to accurately identify NE boundaries. We then evaluate this method automatically using DBpedia as a rich data source of NEs, with the aid of a small representative random sample that is manually annotated. Finally, an analysis of the types of named entities that often occur in a query log is ...
      Read Full Article
    17. Chinese Named Entity Recognition and Disambiguation Based on Wikipedia

      This paper presents a method for named entity recognition and disambiguation based on Wikipedia. First, we establish Wikipedia database using open source tools named JWPL. Second, we extract the definition term from the first sentence of Wikipedia page and use it as external knowledge in named entity recognition. Finally, we achieve named entity disambiguation using Wikipedia disambiguation pages and contextual information. The experiments show that the use of Wikipedia features can improve the accuracy of named entity recognition. Content Type Book ChapterPages 272-283DOI 10.1007/978-3-642-34456-5_25Authors Yu Miao, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy ...
      Read Full Article
    18. Fusion of Long Distance Dependency Features for Chinese Named Entity Recognition Based on Markov Logic Networks

      For the issue that existing methods for Chinese Named Entity Recognition(NER) fail to consider the long-distance dependencies, which is common in the document. This paper, Fusion of long distance dependency, proposes a method for Chinese Named Entity Recognition(NER) based on Markov Logic Networks(MLNs), which comprehensively utilizes local, short distance dependency and long distance dependency features by taking advantage of first order logic to represent knowledge, and then integrates all the features into Markov Network for Chinese named entity recognition with the help of MLNs. Validity of proposed method is verified both in open domain and restricted domain ...
      Read Full Article
    19. A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools

      Abstract Background  We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results  Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions  The finding that ...
      Read Full Article
    20. Extending Enterprise Service Design Knowledge Using Clustering

      Automatically constructing or completing knowledge bases of SOA design knowledge puts traditional clustering approaches beyond their limits. We propose an approach to amend incomplete knowledge bases of Enterprise Service (ES) design knowledge, based on a set of ES signatures. The approach employs clustering, complemented with various filtering and ranking techniques to identify potentially new entities. We implemented and evaluated the approach, and show that it significantly improves the detection of entities compared to a state-of-the-art clustering technique. Ultimately, extending an existing knowledge base with entities is expected to further improve ES search result quality. Content Type Book ChapterPages 142-157DOI 10 ...
      Read Full Article
    21. Using rule-based natural language processing to improve disease normalization in biomedical text.

      Using rule-based natural language processing to improve disease normalization in biomedical text. J Am Med Inform Assoc. 2012 Oct 6; Authors: Kang N, Singh B, Afzal Z, van Mulligen EM, Kors JA Abstract BACKGROUND AND OBJECTIVE: In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization. METHODS: We ...
      Read Full Article
    22. Clique based clustering for named entity recognition system

      A soft clustering method comprises (i) grouping items into non-exclusive cliques based on features associated with the items, and (ii) clustering the non-exclusive cliques using a hard clustering algorithm to generate item groups on the basis of mutual similarity of the features of the items constituting the cliques. In some named entity recognition embodiments illustrated herein as examples, named entities together with contexts are grouped into cliques based on mutual context similarity. Each clique includes a plurality of different named entities having mutual context similarity. The cliques are clustered to generate named entity groups on the basis of mutual similarity ...
      Read Full Article
    23. Improving the Performance of a Named Entity Recognition System with Knowledge Acquisition

      Named Entity Recognition (NER) is important for extracting information from highly heterogeneous web documents. Most NER systems have been developed based on formal documents, but informal web documents usually contain noise, and incorrect and incomplete expressions. The performance of current NER systems drops dramatically as informality increases in web documents and a different kind of NER is needed. Here we propose a Ripple-Down-Rules-based Named Entity Recognition (RDRNER) system. This is a wrapper around the machine-learning-based Stanford NER system, correcting its output using rules added by people to deal with specific application domains. The key advantages of this approach are that ...
      Read Full Article
    73-96 of 371 « 1 2 3 4 5 6 7 ... 14 15 16 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. Popular Articles

  3. Organizations in the News

    1. (1 articles) Language Technology Lab
    2. (1 articles) Karolinska Institute
    3. (1 articles) J Am Med Inform Assoc
    4. (1 articles) NLP
    5. (1 articles) University of Cambridge
    6. (1 articles) Penn Treebank
    7. (1 articles) Named Entity Recognition
    8. (1 articles) NER
  4. Locations in the News

    1. (1 articles) United Kingdom
    2. (1 articles) Sweden
    3. (1 articles) Stockholm
    4. (1 articles) Cambridge