Science Citation Knowledge Extractor

The importance of academic publications is often evaluated by the number of and impact of its subsequent citing works. These citing works build upon the referenced material, representing both further intellectual insights and additional derived uses. As such, reading peer-reviewed articles which cite one's work can serve as a way for authors to understand how their research is being adopted and extended by the greater scientific community, further develop the broader impacts of their research, and even find new collaborators. Unfortunately, in today's rapidly growing and shifting scientific landscape, it is unlikely that a researcher has enough time to read through all articles citing their works, especially in the case of highly-cited broad-impact studies. To address this challenge, we developed the Science Citation Knowledge Extractor (SCKE), a web tool to provide biological and biomedical researchers with an overview of how their work is being utilized by the broader scientific community. SCKE is a web-based tool which utilizes natural language processing and machine learning to retrieve key information from scientific publications citing a given work, analyze the citing material, and present users with interactive data visualizations which illustrate how their works are contributing to greater scientific pursuits. Results are generally grouped into two categories, aimed at (1) understanding the broad scientific areas which one's work is impacting and (2) assessing the breadth and impact of one's work within these areas. As a web application, SCKE is easy to use, with a single input of PubMed ID(s) to analyze. SCKE is available for immediate use by the scientific community as a hosted web application at https://geco.iplantcollaborative.org/scke/. SCKE can also be self-hosted by taking advantage of a fully-integrated VM Image (https://tinyurl.com/y7ggpvaa), Docker container (https://tinyurl.com/y95u9dhw), or open-source code (GPL license) available on GitHub (https://tinyurl.com/yaesue5e)

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

Mining Measured Information from Text

We present an approach to extract measured information from text (e.g., a 1370 degrees C melting point, a BMI greater than 29.9 kg/m^2 ). Such extractions are critically important across a wide range of domains - especially those involving search and exploration of scientific and technical documents. We first propose a rule-based entity extractor to mine measured quantities (i.e., a numeric value paired with a measurement unit), which supports a vast and comprehensive set of both common and obscure measurement units. Our method is highly robust and can correctly recover valid measured quantities even when significant errors are introduced through the process of converting document formats like PDF to plain text. Next, we describe an approach to extracting the properties being measured (e.g., the property "pixel pitch" in the phrase "a pixel pitch as high as 352 {\mu}m"). Finally, we present MQSearch: the realization of a search engine with full support for measured information.Comment: 4 pages; 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15

Learning to Predict Charges for Criminal Cases with Legal Basis

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201