Event Extraction With Complex Event Classification Using Rich Features

Misawa, Miwa; Sætre, Rune; Kim, Jin-Dong; Tsujii, Jun’ichi

doi:10.1142/s0219720010004586

Cited by 135 publications

(109 citation statements)

References 10 publications

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“…Also, a focus on gaining higher mapping accuracy should be included. 32 | P a g e www.ijacsa.thesai.org Some studies focused on event extraction applications in biomedical text, such as [85], [86]. On the other hand, some concentration was held on security-based event extraction applications, such a sin [87].…”

Section: Resultsmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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Abstract-The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

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Section: Resultsmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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show abstract

“…This task helped shift the focus of relation extraction efforts from identifying simple binary interactions to identifying complex nested events that better represent the biological interactions stated frequently in text. Existing approaches to this task include SVM (Björne and Salakoski, 2013) other ML based approaches (Riedel and McCallum, 2011;Miwa et al, 2010Miwa et al, , 2012 ( learn subgraph patterns from the event annotations in the training data and cast the event detection as subgraph matching problem. Non-feature based approaches like graph kernels compare syntactic structures directly (Airola et al, 2008;Bunescu et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Biomedical Event Extraction using Abstract Meaning Representation

Rao¹,

Marcu²,

Knight³

et al. 2017

BioNLP 2017

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We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset 1 (Kim et al., 2013) and show promising results.

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“…Next, we introduce a real-world text-processing workflow in the field of natural language processing -Event Recognition (ER) [39], [40]. We build ER on top of ParaLite, Hadoop (specifically Hadoop Streaming [41]), Hive and general files and discuss their strengths/weaknesses in terms of both productivity and performance for the workflow.…”

Section: Real-world Text Processing Workflowmentioning

confidence: 99%

ParaLite: A Parallel Database System for Data-Intensive Workflows

Chen

Taura

2014

IEICE Trans. Inf. & Syst.

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SUMMARYTo better support data-intensive workflows which are typically built out of various independently developed executables, this paper proposes extensions to parallel database systems called User-Defined eXecutables (UDX) and collective queries. UDX facilitates the description of workflows by enabling seamless integrations of external executables into SQL statements without any efforts to write programs confirming to strict specifications of databases. A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary UDX. It provides efficient parallelization of executables through the data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and computational loads into account. We implement this concept in a system called ParaLite, a parallel database system based on a popular lightweight database SQLite. Our experiments show that ParaLite has several times higher performance over Hive for typical SQL tasks and has 10x speedup compared to a commercial DBMS for executables. In addition, this paper studies a real-world text processing workflow and builds it on top of ParaLite, Hadoop, Hive and general files. Our experiences indicate that ParaLite outperforms other systems in both productivity and performance for the workflow. key words: data-intensive workflow, parallel database system, userdefined executable, collective query

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Event Extraction With Complex Event Classification Using Rich Features

Cited by 135 publications

References 10 publications

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Biomedical Event Extraction using Abstract Meaning Representation

ParaLite: A Parallel Database System for Data-Intensive Workflows

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