CERMINE is a comprehensive open-source system for extracting structured metadata from scientific articles in a born-digital form. The system is based on a modular workflow, whose loosely coupled architecture allows for individual component evaluation and adjustment, enables effortless improvements and replacements of independent parts of the algorithm and facilitates future architecture expanding. The implementations of most steps are based on supervised and unsupervised machine learning techniques, which simplifies the procedure of adapting the system to new document layouts and styles. The evaluation of the extraction workflow carried out with the use of a large dataset showed good performance for most metadata types, with the average F score of 77.5 %. CERMINE system is available under an open-source licence and can be accessed at http://cermine.ceon.pl. In this paper, we outline the overall workflow architecture and provide details about individual steps implementations. We also thoroughly compare CERMINE to similar solutions, describe evaluation methodology and finally report its results.

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CERMINE: automatic extraction of structured metadata from scientific literature

Systems and methods for extracting data content items from a web page are provided. A template is created by labeling data content items of interest associated with a web page and generating a template Document Object Model (DOM) tree based on the labeled web page. DOM trees are also generated for additional web pages that contain data content items for which extraction may be desired. These DOM trees are compared to the template DOM tree to determine alignment there between. The aligned data content items may then be extracted from the additional web pages and indexed, as desired. Labeling the data content items of interest prior to generating a template DOM tree allows for the desired data content items to be specified and more accurately extracted from related and/or similarly structured web pages.

Extracting data content items using template matching

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naive-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Information extraction from scientific articles: a survey

This paper describes a simple method for extracting metadata fields from citations using hidden Markov models. The method is easy to implement and can achieve levels of precision and recall for heterogeneous citations comparable to or greater than other HMM-based methods. The method consists largely of string manipulation and otherwise depends only on an implementation of the Viterbi algorithm, which is widely available, and so can be implemented by diverse digital library systems.

A simple method for citation metadata extraction using hidden markov models

Some figures in this document are best viewed in colour. If you received a black-and-white copy, please consult the online version if necessary. Summary While there has been growing interest in the field of sentiment analysis for different text genres in the past few years, relatively less emphasis has been placed on extraction of opinions from scientific literature, more specifically, citations. Citation sentiment detection is an attractive task as it can help researchers in identifying shortcomings and detecting problems in a particular approach, determining the quality of a paper for ranking in citation indexes by including negative citations in the weighting scheme, and recog-nising issues that have not been addressed as well as possible gaps in current research approaches. Current approaches assume that the sentiment present in the citation sentence represents the true sentiment of the author towards the cited paper and do not take further informal mentions of the citations elsewhere in the article into account. There have also been no attempts to evaluate citation sentiment on a large corpus. This dissertation focuses on the detection of sentiment towards the citations in a scientific article. The detection is performed using the textual information from the article. I address three sub-tasks and present new large corpora for each of the tasks. Firstly, I explore different feature sets for detection of sentiment in explicit citations. For this task, I present a new annotated corpus of more than 8,700 citation sentences which have been labelled as positive, negative or objective towards the cited paper. Experimenting with different feature sets, I show the best result of micro-F score 0.760 is obtained using n-grams of length and dependency relations. Secondly, I show that the assumption that sentiment is limited only to the explicit citation is incorrect. I present a citation context corpus where more than 200,000 sentences from 1,034 paper–reference pairs have been annotated for sentiment. These sentences contain 1,741 citations towards 20 cited papers. I show that including the citation context in the analysis increases the subjective sentiment by almost 185%. I propose new features which help in extracting the citation context and examine their effect on sentiment analysis. Thirdly, I tackle the task of identifying significant citations. I propose features which help discriminate such citations from citations in passing, and show that they provide statistically significant improvements over a rule-based baseline. Acknowledgments This dissertation would not have been possible without the help …

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Sentiment analysis of scientific citations

The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.

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Reference metadata extraction using a hierarchical knowledge representation framework

In order to build an automatic named entity recognition (NER) system for machine learning, a large tagged corpus is necessary This paper describes the manual construction of a Chinese named entity tagged corpus (CNEC 10) that can be used to improve NER performance In this project, we define five named entity tags: PER (person name), LOC (location name), ORG (organization name), LAO (location as organization), and OAL (organization as location) for named entity categories In addition, we propose a special tag, DIFF (Difficulty), to annotate ambiguous cases during corpus construction A, corpus-annotating procedure, a tagging tool, and an original corpus are also introduced Finally, we demonstrate a part of our manual-tagged corpus

https://www.csie.cyut.edu.tw/~shwu/publication/rocling2004Shih.pdf

The Construction of a Chinese Named Entity Tagged Corpus: CNEC1.0

Intelligent tutoring systems (ITSs) construction requires lots of domain knowledge created by hand In this paper we attempt to illustrate a central role that knowledge representation can play in automating ITS design and implementation We propose a diagnosis, interaction and treatment (DIT) model for ITS The entire system relies upon a knowledge representation system (InfoMap) whose structured encoding of English lexical information makes it possible to (1) initiate the relevant dialogue with learners when the system is not sure about learners' intentions, (2) trigger the appropriate lexical knowledge based on their responses, and (3) automatically generate practice and test exercises based on this knowledge

http://iasl.iis.sinica.edu.tw/webpdf/paper-2002-Exploiting_Knowledge_Representation_in_an_.pdf

Chiu-Chen Hsieh

Papers

Reference metadata extraction using a hierarchical knowledge representation framework

The Construction of a Chinese Named Entity Tagged Corpus: CNEC1.0

Exploiting knowledge representation in an intelligent tutoring system for English lexical errors