Information Extraction System Research Articles

Using automatically extracted information from mammography reports for decision-support

ObjectiveTo evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methodsWe built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. ResultsThe NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004±0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. ConclusionThe accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.

Annotation Approach for Document with Recommendation

An enormous number of organizations generate and share textual descriptions of their products, facilities, and activities. Such collections of textual data comprise a significant amount of controlled information, which residues buried in the unstructured text. Whereas information extraction systems simplify the extraction of structured associations, they are frequently expensive and incorrect, particularly when working on top of text that does not comprise any examples of the targeted structured data. Projected an alternative methodology that simplifies the structured metadata generation by recognizing documents that are possible to contain information of awareness and this data will be beneficial for querying the database. Moreover, we intend algorithms to extract attribute-value pairs, and similarly devise new mechanisms to map such pairs to manually created schemes. We apply clustering technique to the item content information to complement the user rating information, which improves the correctness of collaborative similarity, and solves the cold start problem.

@MInter: automated text-mining of microbial interactions.

Microbial consortia are frequently defined by numerous interactions within the community that are key to understanding their function. While microbial interactions have been extensively studied experimentally, information regarding them is dispersed in the scientific literature. As manual collation is an infeasible option, automated data processing tools are needed to make this information easily accessible. We present @MInter, an automated information extraction system based on Support Vector Machines to analyze paper abstracts and infer microbial interactions. @MInter was trained and tested on a manually curated gold standard dataset of 735 species interactions and 3917 annotated abstracts, constructed as part of this study. Cross-validation analysis showed that @MInter was able to detect abstracts pertaining to one or more microbial interactions with high specificity (specificity = 95%, AUC = 0.97). Despite challenges in identifying specific microbial interactions in an abstract (interaction level recall = 95%, precision = 25%), @MInter was shown to reduce annotator workload 13-fold compared to alternate approaches. Applying @MInter to 175 bacterial species abundant on human skin, we identified a network of 357 literature-reported microbial interactions, demonstrating its utility for the study of microbial communities. @MInter is freely available at https://github.com/CSB5/atminter nagarajann@gis.a-star.edu.sg Supplementary data are available at Bioinformatics online.

Extracting Databases from Dark Data with DeepDive.

DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

MONGOOSE-Monitoring Global Online Opinions via Semantic Extraction

MONGOOSE is a strategy which separates client assessments that are executing this method for web shopping webpage gives data about best items in all fields like dresses,mobiles,jewellery and blessing articles based upon client rating and conclusion. It is relies on upon the client assessment to include or evacuate the items in our Website. In this data is accumulated for nothing and open sources on the web as often as possible integrated.This is a methodology that looks to lessen the time spends on making a steady information. It lessens an ideal opportunity to-effect of cutting edge investigation administration arrangements. Consumers are often forced to wade through an alarming number of online reviews in order to make an informed product choice .This paper introduces opinion extraction, an unsupervised information-extraction system that mines product reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across different product instances. When compared to previous work, it achieves 22% higher precision (at the cost of 3% lower recall) on feature extraction. In addition, it reports an 8% improvement in accuracy on the task of determining whether an opinion sentence is positive or negative. OPINE’s success comes from a more comprehensive effort to identify product features, which enables it to augment review opinions with background information extracted from the Web.

Learning to extract domain-specific relations from complex sentences

Open Information Extraction (OIE) systems focus on identifying and extracting general relations from text. Most OIE systems utilize simple linguistic structure, such as part-of-speech or dependency features, to extract relations and arguments from a sentence. These approaches are simple and fast to implement, but suffer from two main drawbacks: i) they are less effective to handle complex sentences with multiple relations and shared arguments, and ii) they tend to extract overly-specific relations.This paper proposes an approach to Information Extraction called SemIE, which addresses both drawbacks. SemIE identifies significant relations from domain-specific text by utilizing a semantic structure that describes the domain of discourse. SemIE exploits the predicate-argument structure of a text, which is able to handle complex sentences. The semantics of the arguments are explicitly specified by mapping them to relevant concepts in the semantic structure.SemIE uses a semi-supervised learning approach to bootstrap training examples that cover all relations expressed in the semantic structure. SemIE inputs pairs of structured documents and uses a Greedy Mapping module to bootstrap a full set of training examples. The training examples are then used to learn the extraction and mapping rules.We evaluated the performance of SemIE by comparing it with OLLIE, a state-of-the-art OIE system. We tested SemIE and OLLIE on the task of extracting relations from text in the “movie” domain and found that on average, SemIE outperforms OLLIE. Furthermore, we also examined how the performance varies with sentence complexity and sentence length. The results prove the effectiveness of SemIE in handling complex sentences.

Automatic Discovery of Abnormal Values in Large Textual Databases

Textual databases are ubiquitous in many application domains. Examples of textual data range from names and addresses of customers to social media posts and bibliographic records. With online services, individuals are increasingly required to enter their personal details for example when purchasing products online or registering for government services, while many social network and e-commerce sites allow users to post short comments. Many online sites leave open the possibility for people to enter unintended or malicious abnormal values, such as names with errors, bogus values, profane comments, or random character sequences. In other applications, such as online bibliographic databases or comparative online shopping sites, databases are increasingly populated in (semi-) automatic ways through Web crawls. This practice can result in low quality data being added automatically into a database. In this article, we develop three techniques to automatically discover abnormal (unexpected or unusual) values in large textual databases. Following recent work in categorical outlier detection, our assumption is that “normal” values are those that occur frequently in a database, while an individual abnormal value is rare. Our techniques are unsupervised and address the challenge of discovering abnormal values as an outlier detection problem. Our first technique is a basic but efficient q-gram set based technique, the second is based on a probabilistic language model, and the third employs morphological word features to train a one-class support vector machine classifier. Our aim is to investigate and develop techniques that are fast, efficient, and automatic. The output of our techniques can help in the development of rule-based data cleaning and information extraction systems, or be used as training data for further supervised data cleaning procedures. We evaluate our techniques on four large real-world datasets from different domains: two US voter registration databases containing personal details, the 2013 KDD Cup dataset of bibliographic records, and the SNAP Memetracker dataset of phrases from social networking sites. Our results show that our techniques can efficiently and automatically discover abnormal textual values, allowing an organization to conduct efficient data exploration, and improve the quality of their textual databases without the need of requiring explicit training data.

Declarative Cleaning of Inconsistencies in Information Extraction

The population of a predefined relational schema from textual content, commonly known as Information Extraction (IE), is a pervasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that unambiguously extract the sought information. For example, during extraction, an IE program could annotate a substring as both an address and a person name. When this happens, the extracted information is said to be inconsistent , and some way of removing inconsistencies is crucial to compute the final output. Industrial-strength IE systems like GATE and IBM SystemT therefore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, however, are collected in an ad hoc fashion through use cases. Ideally, we would like to allow IE developers to declare their own policies. But existing cleaning operations are defined in an algorithmic way, and hence it is not clear how to extend the built-in operations without requiring low-level coding of internal or external functions. We embark on the establishment of a framework for declarative cleaning of inconsistencies in IE through principles of database theory. Specifically, building upon the formalism of document spanners for IE, we adopt the concept of prioritized repairs , which has been recently proposed as an extension of the traditional database repairs to incorporate priorities among conflicting facts. We show that our framework captures the popular cleaning policies, as well as the POSIX semantics for extraction through regular expressions. We explore the problem of determining whether a cleaning declaration is unambiguous (i.e., always results in a single repair) and whether it increases the expressive power of the extraction language. We give both positive and negative results, some of which are general and some of which apply to policies used in practice.

PDF text classification to leverage information extraction from publication reports

ObjectivesData extraction from original study reports is a time-consuming, error-prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi-structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems. MethodsWe used an open-source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories. To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions. ResultsThe multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F-measure improvements were observed in the classification of TITLE (+15.6%), ABSTRACT (+54.2%), BODYTEXT (+3.7%), SEMISTRUCTURE (+34%), and MEDADATA (+14.2%). In addition, use of the algorithm to filter semi-structured texts and publication metadata improved performance of the outcome extraction system (F-measure +4.1%, p=0.002). It also reduced of number of sentences to be processed by 44.9% (p<0.001), which corresponds to a processing time reduction of 50% (p=0.005). ConclusionsThe rule-based multi-pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents.

A hybrid integrated architecture for energy consumption prediction

Irresponsible and negligent use of natural resources in the last five decades has made it an important priority to adopt more intelligent ways of managing existing resources, especially the ones related to energy. The main objective of this paper is to explore the opportunities of integrating internal data already stored in Data Warehouses together with external Big Data to improve energy consumption predictions. This paper presents a study in which we propose an architecture that makes use of already stored energy data and external unstructured information to improve knowledge acquisition and allow managers to make better decisions. This external knowledge is represented by a torrent of information that, in many cases, is hidden across heterogeneous and unstructured data sources, which are recuperated by an Information Extraction system. Alternatively, it is present in social networks expressed as user opinions. Furthermore, our approach applies data mining techniques to exploit the already integrated data. Our approach has been applied to a real case study and shows promising results. The experiments carried out in this work are twofold: (i) using and comparing diverse Artificial Intelligence methods, and (ii) validating our approach with data sources integration.

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov

Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov. We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base. The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy.

Name identification and extraction with formal concept analysis

One of the applications of the Formal concept analysis (FCA) is the ability to extract structured information from textual documents. Typically, one can define a set of attributes that will characterize the objects. Consequently, these defined objects will be extracted by standard FCA algorithms. In this paper, we describe how FCA identifies and extracts personal names as units of thought similar to the decoding of text sequences by Viterbi algorithm as used with Hidden Markov Models. We further exhibit how FCA mimics the thought process that goes into a rule-based information extraction system. We then observe that the formal approach of FCA combined with already established computational techniques such as bottom up intersection algorithm avoids the difficulties associated with hand coding and maintenance of rule-based systems.

Statistic Supported Cooperative Creation of Training Corpora for the Extraction of Traffic Information from Microblogs

Microblogs like Twitter1 are sources for a lot of useful information. But these information are unstructured. To automatically process it, these information have to be extracted with the help of natural language processing techniques. Corresponding systems for information extraction have to be trained and validated with manual annotated data. This paper presents an approach for the cooperative creation of annotated corpora for training and validation of information extraction systems supported by statistical analyses.1https://twitter.com/

Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning.

Medicinal chemistry patents contain rich information about chemical compounds. Although much effort has been devoted to extracting chemical entities from scientific literature, limited numbers of patent mining systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent text (CHEMDNER patents). This track included three individual subtasks: (i) Chemical Entity Mention Recognition in Patents (CEMP), (ii) Chemical Passage Detection (CPD) and (iii) Gene and Protein Related Object task (GPRO). We participated in the two subtasks of CEMP and CPD using machine learning-based systems. Our machine learning-based systems employed the algorithms of conditional random fields (CRF) and structured support vector machines (SSVMs), respectively. To improve the performance of the NER systems, two strategies were proposed for feature engineering: (i) domain knowledge features of dictionaries, chemical structural patterns and semantic type information present in the context of the candidate chemical and (ii) unsupervised feature learning algorithms to generate word representation features by Brown clustering and a novel binarized Word embedding to enhance the generalizability of the system. Further, the system output for the CPD task was yielded based on the patent titles and abstracts with chemicals recognized in the CEMP task.The effects of the proposed feature strategies on both the machine learning-based systems were investigated. Our best system achieved the second best performance among 21 participating teams in CEMP with a precision of 87.18%, a recall of 90.78% and a F-measure of 88.94% and was the top performing system among nine participating teams in CPD with a sensitivity of 98.60%, a specificity of 87.21%, an accuracy of 94.75%, a Matthew’s correlation coefficient (MCC) of 88.24%, a precision at full recall (P_full_R) of 66.57% and an area under the precision-recall curve (AUC_PR) of 0.9347. The SSVM-based CEMP systems outperformed the CRF-based CEMP systems when using the same features. Features generated from both the domain knowledge and unsupervised learning algorithms significantly improved the chemical NER task on patents.Database URL: http:// database. oxfordjournals. org/ content/ 2016/ baw049

Information extraction for personalised services based on conference alerts

Text mining is moderately new research area at the interaction of data mining, natural language processing (NLP), machine learning and information retrieval. The interconnected task, information extraction is a text transforming that places a specified set of significant items in a natural-language document. It distils organised data or knowledge from unstructured text by recognising references to named entities and additionally expressed relationships between such entities. We present a new schema for text mining as information extraction for prediction, which uses a learn information extraction system to transform text into more structures data which is then be further analysed or mine for discovering more general patterns and interesting relationships. This paper presents the work obtained by applying information extraction (IE) technique to a corpus of conference announcement posted on conference web newsgroups. The work is analysis of extracted essential name entities that were used to find the patterns of recent trends in research area and it also provide a platform to explore more on NLP aspects.

Temporal Expressions in Polish Corpus KPWr

Temporal Expressions in Polish Corpus KPWrThis article presents the result of the recent research in the interpretation of Polish expressions that refer to time. These expressions are the source of information when something happens, how often something occurs or how long something lasts. Temporal information, which can be extracted from text automatically, plays significant role in many information extraction systems, such as question answering, discourse analysis, event recognition and many more. We prepared PLIMEX — a broad description of Polish temporal expressions with annotation guidelines, based on the state-of-the-art solutions for English, mainly TimeML specification. We also adapted the solution to capture the local semantics of temporal expressions, called LTIMEX. Temporal description also supports further event identification and extends event description model, focusing at anchoring events in time, ordering events and reasoning about the persistence of events. We prepared the specification, which is designed to address these issues and we annotated all documents in Polish Corpus of Wroclaw University of Technology (KPWr) using our annotation guidelines.

A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.

Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network.

Survey of Stages of Developing the Information Extraction Systems from the Web

Survey of Stages of Developing the Information Extraction Systems from the Web

Fine-grained information extraction from German transthoracic echocardiography reports.

BackgroundInformation extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg.MethodsA domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts.ResultsThe final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f1=.989 (micro average) and f1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout.ConclusionsThe developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports with very high precision and high recall on the majority of documents at the University Hospital of Würzburg. Extracted results populate a clinical data warehouse which supports clinical research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0215-x) contains supplementary material, which is available to authorized users.

An Approach for Ontology-Based Information Extraction System Selection and Evaluation

An Approach for Ontology-Based Information Extraction System Selection and Evaluation