Discourse Processing for Text Mining

2010 ◽  
pp. 222-254
Author(s):  
Nadine Lucas
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
Text Processing ◽  
Discourse Processing ◽  
Biomedical Literature ◽  
Manual Annotation ◽  
Text Types ◽  
Granularity Level ◽  
Document Mining ◽  
Domain Independent

This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the trees”. Characterisation tasks aimed at filtering collections are introduced, showing use of the whole set of layout constituents from sentence to text body. Few text descriptors encapsulating knowledge on text properties are used for each granularity level. Text processing differs according to tasks, whether individual document mining or tagging small or large collections prior to information extraction. Very shallow and domain independent techniques are used to tag collections to save costs on sentence parsing and semantic manual annotation. This approach achieves satisfactory characterisation of text types, for example reviews versus clinical reports, or argumentation-type articles versus explanation-type. These collection filtering techniques are fit for a wider domain of biomedical literature than genomics.

Information Extraction of Protein Phosphorylation from Biomedical Literature

2010 ◽  
pp. 163-176
Author(s):  
M. Narayanaswamy ◽  
K. E. Ravikumar ◽  
Z. Z. Hu ◽  
K. Vijay-Shanker ◽  
C. H. Wu
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
Protein Phosphorylation ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Second Phase ◽  
Mining System ◽  
Rule Based ◽  
Kinase Substrate ◽  
Fundamental Biological Process

Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information extraction. A rule-based text mining system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was recently developed by our group to extract protein substrate, kinase and phosphorylated residue/sites from MEDLINE abstracts. This chapter covers the evaluation and benchmarking of RLIMS-P and highlights some novel and unique features of the system. The extraction patterns of RLIMS-P capture a range of lexical, syntactic and semantic constraints found in sentences expressing phosphorylation information. RLIMS-P also has a second phase that puts together information extracted from different sentences. This is an important feature since it is not common to find the kinase, substrate and site of phosphorylation to be mentioned in the same sentence. Small modifications to the rules for extraction of phosphorylation information have also allowed us to develop systems for extraction of two other PTMs, acetylation and methylation. A thorough evaluation of these two systems needs to be completed. Finally, an online version of RLIMSP with enhanced functionalities, namely, phosphorylation annotation ranking, evidence tagging, and protein entity mapping, has been developed and is publicly accessible.

Support Vector Machine VS Information Gain: Analisis Sentimen Cyberbullying di Twitter Indonesia

2020 ◽  
Vol 11 (2) ◽  
pp. 107-111
Author(s):  
Christevan Destitus ◽  
Wella Wella ◽  
Suryasari Suryasari
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Mining ◽  
Information Gain ◽  
Text Processing ◽  
Support Vector ◽  
Term Weighting ◽  
System Process ◽  
Research Stage

This study aims to clarify tweets on twitter using the Support Vector Machine and Information Gain methods. The clarification itself aims to find a hyperplane that separates the negative and positive classes. In the research stage, there is a system process, namely text mining, text processing which has stages of tokenizing, filtering, stemming, and term weighting. After that, a feature selection is made by information gain which calculates the entropy value of each word. After that, clarify based on the features that have been selected and the output is in the form of identifying whether the tweet is bully or not. The results of this study found that the Support Vector Machine and Information Gain methods have sufficiently maximum results.

Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

2019 ◽  
pp. 41-64
Author(s):  
Logeswari Shanmugam ◽  
Premalatha K.
Keyword(s):  
Text Processing ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Data Sets ◽  
Original Text ◽  
Biomedical Knowledge ◽  
Text Data ◽  
Natural Language Text ◽  
Diverse Data ◽  
Medical Document

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.

Information Extraction in Biomedical Literature

2011 ◽  
pp. 615-620
Author(s):  
Min Song ◽  
Il-Yeol Song ◽  
Xiaohua Hu ◽  
Hyoil Han
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Latin American ◽  
Relational Databases ◽  
Biomedical Literature ◽  
Text Documents ◽  
Plain Text ◽  
Natural Language Text ◽  
Relational Form ◽  
Structured Representation

Information extraction (IE) technology has been defined and developed through the US DARPA Message Understanding Conferences (MUCs). IE refers to the identification of instances of particular events and relationships from unstructured natural language text documents into a structured representation or relational table in databases. It has proved successful at extracting information from various domains, such as the Latin American terrorism, to identify patterns related to terrorist activities (MUC-4). Another domain, in the light of exploiting the wealth of natural language documents, is to extract the knowledge or information from these unstructured plain-text files into a structured or relational form. This form is suitable for sophisticated query processing, for integration with relational databases, and for data mining. Thus, IE is a crucial step for fully making text files more easily accessible.

Information Extraction for Call for Paper

2015 ◽  
Vol 6 (4) ◽  
pp. 35-49 ◽  
Author(s):  
Laurent Issertial ◽  
Hiroshi Tsuji
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
False Positive ◽  
Relevant Information ◽  
End User ◽  
Rule Based ◽  
Complex Queries ◽  
Mining Algorithm ◽  
Text Searching ◽  
Searching Method

This paper proposes a system called CFP Manager specialized on IT field and designed to ease the process of searching conference suitable to one's need. At present, the handling of CFP faces two problems: for emails, the huge quantity of CFP received can be easily skimmed through. For websites, the reviewing of some of the main CFP aggregators available online points out the lack of usable criteria. This system proposes to answer to these problems via its architecture consisting of three components: firstly an Information Extraction module extracting relevant information (as date, location, etc...) from CFP using rule based text mining algorithm. The second component enriches the now extracted data with external one from ontology models. Finally the last one displays the said data and allows the end user to perform complex queries on the CFP dataset and thus allow him to only access to CFP suitable for him. In order to validate the authors' proposal, they eventually process the well-known precision / recall metric on our information extraction component with an average of 0.95 for precision and 0.91 for recall on three different 100 CFP dataset. This paper finally discusses the validity of our approach by confronting our system for different queries with two systems already available online (WikiCFP and IEEE Conference Search) and basic text searching approach standing for searching in an email box. On a 100 CFP dataset with the wide variety of usable data and the possibility to perform complex queries we surpass basic text searching method and WikiCFP by not returning the false positive usually returned by them and find a result close to the IEEE system.

Models of Discourse Representation

2021 ◽  
pp. 276-356
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Language Processing ◽  
Representative Sample ◽  
Text Processing ◽  
Discourse Processing ◽  
Discourse Representation ◽  
Ongoing Debate ◽  
Large Representative

This chapter first describes what has been learned about how readers represent the meaning of discourse by integrating the meanings to individual sentences to construct the representations needed to understand larger segments of text. The chapter reviews the key findings related to text processing and how this sparked an ongoing debate about the extent to which the making of inferences during reading is obligatory. The chapter reviews precursor theories and models of discourse representation that attempt to explain how discourse representations are generated via the interaction of language processing and memory. The chapter then reviews a large, representative sample of the models that have been used to simulate and understand aspects of discourse processing. They are reviewed in their order of development to show how the models have evolved to accommodate new empirical findings. The chapter concludes with an explicit comparative analysis of the discourse-processing models and discusses the empirical findings that each model can and cannot explain.

scholarly journals A Novel Text-Mining Approach for Retrieving Pharmacogenomics Associations From the Literature

2020 ◽  
Vol 11 ◽  
Author(s):  
Maria-Theodora Pandi ◽  
Peter J. van der Spek ◽  
Maria Koromina ◽  
George P. Patrinos
Keyword(s):  
Text Mining ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Biomedical Literature ◽  
Linear Kernel ◽  
R Programming Language ◽  
Research Areas ◽  
Text Classifiers ◽  
R Programming ◽  
Further Development

Text mining in biomedical literature is an emerging field which has already been shown to have a variety of implementations in many research areas, including genetics, personalized medicine, and pharmacogenomics. In this study, we describe a novel text-mining approach for the extraction of pharmacogenomics associations. The code that was used toward this end was implemented using R programming language, either through custom scripts, where needed, or through utilizing functions from existing libraries. Articles (abstracts or full texts) that correspond to a specified query were extracted from PubMed, while concept annotations were derived by PubTator Central. Terms that denote a Mutation or a Gene as well as Chemical compound terms corresponding to drug compounds were normalized and the sentences containing the aforementioned terms were filtered and preprocessed to create appropriate training sets. Finally, after training and adequate hyperparameter tuning, four text classifiers were created and evaluated (FastText, Linear kernel SVMs, XGBoost, Lasso, and Elastic-Net Regularized Generalized Linear Models) with regard to their performance in identifying pharmacogenomics associations. Although further improvements are essential toward proper implementation of this text-mining approach in the clinical practice, our study stands as a comprehensive, simplified, and up-to-date approach for the identification and assessment of research articles enriched in clinically relevant pharmacogenomics relationships. Furthermore, this work highlights a series of challenges concerning the effective application of text mining in biomedical literature, whose resolution could substantially contribute to the further development of this field.

scholarly journals BioReader: a text mining tool for performing classification of biomedical literature

2019 ◽  
Vol 19 (S13) ◽  
Author(s):  
Christian Simon ◽  
Kristian Davidsen ◽  
Christina Hansen ◽  
Emily Seymour ◽  
Mike Bogetofte Barnkob ◽  
...  
Keyword(s):  
Text Mining ◽  
Biomedical Literature ◽  
Mining Tool ◽  
Text Mining Tool
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