1. Articles in category: NER

    73-96 of 381 « 1 2 3 4 5 6 7 ... 14 15 16 »
    1. A Comparison of Named Entity Recognition Tools Applied to Biographical Texts. (arXiv:1308.0661v1 [cs.IR])

      Named entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts.

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    2. Using Empirically Constructed Lexical Resources for Named Entity Recognition.

      Using Empirically Constructed Lexical Resources for Named Entity Recognition. Biomed Inform Insights. 2013;6(Suppl 1):17-27 Authors: Jonnalagadda S, Cohen T, Wu S, Liu H, Gonzalez G Abstract Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small-annotated corpora might not have sufficient examples for learning to statistically extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and ...
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    3. IMS Health Advances Cloud-Based Social Media Analytics with Acquisition of Semantelli

      DANBURY, Conn.--(BUSINESS WIRE)--IMS Health today announced the acquisition of Semantelli Corporation, a Bridgewater, N.J.-based social media analytics company, to extend its marketing and consumer engagement capabilities for healthcare organizations around the world. Semantelli offers clients a robust set of cloud-based tools that automate the collection of healthcare-specific social media ...
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    4. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.

      Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Med Inform Decis Mak. 2013;13 Suppl 1:S1 Authors: Tang B, Cao H, Wu Y, Jiang M, Xu H Abstract BACKGROUND: Named entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which ...
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    5. Extracting drug indication information from structured product labels using natural language processing.

      Extracting drug indication information from structured product labels using natural language processing. J Am Med Inform Assoc. 2013 Mar 9; Authors: Fung KW, Jao CS, Demner-Fushman D Abstract OBJECTIVE: To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies. MATERIALS AND METHODS: We used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the ...
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    6. ChemSpot: a hybrid system for chemical named entity recognition.

      Related Articles ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics. 2012 Jun 15;28(12):1633-40 Authors: Rocktäschel T, Weidlich M, Leser U Abstract MOTIVATION: The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools. RESULTS: We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial ...
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    7. 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 ...
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      Mentions: Treebank Lee V Palmer
    8. 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 ...
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    9. 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 ...
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    10. 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 ...
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    11. 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 ...
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    12. 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 ...
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      Mentions: Beijing China Medline
    13. 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 ...
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    14. 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 ...
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      Mentions: Medline Lee V Shah
    15. 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
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    16. 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 ...
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    17. 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 ...
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    18. 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 ...
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    19. 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 ...
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    20. 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 ...
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    73-96 of 381 « 1 2 3 4 5 6 7 ... 14 15 16 »
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