532 research outputs found
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 āappropriateā 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
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be deļ¬ned by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The ļ¬ndings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
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 āappropriateā 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
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
Social media platforms are vital resources for sharing self-reported health
experiences, offering rich data on various health topics. Despite advancements
in Natural Language Processing (NLP) enabling large-scale social media data
analysis, a gap remains in applying keyphrase extraction to health-related
content. Keyphrase extraction is used to identify salient concepts in social
media discourse without being constrained by predefined entity classes. This
paper introduces a theme-driven keyphrase extraction framework tailored for
social media, a pioneering approach designed to capture clinically relevant
keyphrases from user-generated health texts. Themes are defined as broad
categories determined by the objectives of the extraction task. We formulate
this novel task of theme-driven keyphrase extraction and demonstrate its
potential for efficiently mining social media text for the use case of
treatment for opioid use disorder. This paper leverages qualitative and
quantitative analysis to demonstrate the feasibility of extracting actionable
insights from social media data and efficiently extracting keyphrases using
minimally supervised NLP models. Our contributions include the development of a
novel data collection and curation framework for theme-driven keyphrase
extraction and the creation of MOUD-Keyphrase, the first dataset of its kind
comprising human-annotated keyphrases from a Reddit community. We also identify
the scope of minimally supervised NLP models to extract keyphrases from social
media data efficiently. Lastly, we found that a large language model (ChatGPT)
outperforms unsupervised keyphrase extraction models, and we evaluate its
efficacy in this task.Comment: 11 pages, 2 figures, submitted to ICWSM. This version represents a
substantial expansion and refocus of the previous manuscript, including new
experiments, expanded data analysis, and comprehensive discussion
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models
Keyphrase Generation (KPG) is a longstanding task in NLP with widespread
applications. The advent of sequence-to-sequence (seq2seq) pre-trained language
models (PLMs) has ushered in a transformative era for KPG, yielding promising
performance improvements. However, many design decisions remain unexplored and
are often made arbitrarily. This paper undertakes a systematic analysis of the
influence of model selection and decoding strategies on PLM-based KPG. We begin
by elucidating why seq2seq PLMs are apt for KPG, anchored by an
attention-driven hypothesis. We then establish that conventional wisdom for
selecting seq2seq PLMs lacks depth: (1) merely increasing model size or
performing task-specific adaptation is not parameter-efficient; (2) although
combining in-domain pre-training with task adaptation benefits KPG, it does
partially hinder generalization. Regarding decoding, we demonstrate that while
greedy search achieves strong F1 scores, it lags in recall compared with
sampling-based methods. Based on these insights, we propose DeSel, a
likelihood-based decode-select algorithm for seq2seq PLMs. DeSel improves
greedy search by an average of 4.7% semantic F1 across five datasets. Our
collective findings pave the way for deeper future investigations into
PLM-based KPG.Comment: EMNLP 2023 camera read
Keyphrase Generation: A Multi-Aspect Survey
Extractive keyphrase generation research has been around since the nineties,
but the more advanced abstractive approach based on the encoder-decoder
framework and sequence-to-sequence learning has been explored only recently. In
fact, more than a dozen of abstractive methods have been proposed in the last
three years, producing meaningful keyphrases and achieving state-of-the-art
scores. In this survey, we examine various aspects of the extractive keyphrase
generation methods and focus mostly on the more recent abstractive methods that
are based on neural networks. We pay particular attention to the mechanisms
that have driven the perfection of the later. A huge collection of scientific
article metadata and the corresponding keyphrases is created and released for
the research community. We also present various keyphrase generation and text
summarization research patterns and trends of the last two decades.Comment: 10 pages, 5 tables. Published in proceedings of FRUCT 2019, the 25th
Conference of the Open Innovations Association FRUCT, Helsinki, Finlan
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