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Multidimensional Coding of Multimodal Languaging in Multi-Party Settings ...
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Prosodic Morphology: Constraint Interaction and Satisfaction ...
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Paraguayan Guaraní and the typology of free affix order
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In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5159 ; 2473-8689 (2022)
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies and References of the Related Work ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies and References of the Related Work. ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies ...
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Supplemental Material: Instantiation of the Proposed Templates in the Multiple Case Study Using CAESAR LaTeX Template ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies and References of the Related Work ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies_V 1.2 ...
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Supplemental Material: Instantiation of the Proposed Templates in the Multiple Case Study Using CAESAR LaTeX Template ...
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Supplemental Material: Instantiation of the Proposed Templates in the Multiple Case Study Using CAESAR LaTeX Template ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies and References of the Related Work. ...
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Supplemental Material: Instantiation of the Proposed Template in the multiple-case studies_V 1.2 ...
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| 14 |
Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency.
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| 15 |
Multi-Input Strictly Local Functions for Templatic Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Templatic morphology through syntactic selection: Valency-changing extensions in Kinyarwanda
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In: Glossa: a journal of general linguistics; Vol 4, No 1 (2019); 112 ; 2397-1835 (2019)
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CLIL teachers’ materials: Sciencetextbook’s evaluative template
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Practical Natural Language Generation from Knowledge Graphs
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In: Embargoed Honors Theses, University of Nebraska-Lincoln (2019)
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Проблеми автоматизованого породження англомовних резюме ; ПРОБЛЕМЫ АВТОМАТИЗИРОВАННОГО ПОРОЖДЕНИЯ АНГЛОЯЗЫЧНЫХ РЕЗЮМЕ ; ISSUES OF AUTOMATED GENERATION OF ENGLISH RESUMES
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In: Вісник Національного технічного університету «ХПІ». Актуальні проблеми розвитку українського суспільства; № 4 (1280) (2018); 81-84 ; Bulletin of the National Technical University "KhPI". Series: Actual problems of Ukrainian society development; № 4 (1280) (2018); 81-84 ; 2227-6890 (2018)
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Big Data Text Summarization - Hurricane Harvey
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Abstract:
Natural language processing (NLP) has advanced in recent years. Accordingly, we present progressively more complex generated text summaries on the topic Hurricane Harvey. We utilized TextRank, which is an unsupervised extractive summarization algorithm. TextRank is computationally expensive, and the sentences generated by the algorithm arent always directly related or essential to the topic at hand. When evaluating TextRank, we found that a single sentence interjected and ruined the flow of the summary. We also found that ROUGE evaluation for our TextRank summary was quite low compared to a golden standard that was prepared for us. However, the TextRank summary had high marks for ROUGE evaluation compared to the Wikipedia article lead for Hurricane Harvey. To improve upon the TextRank algorithm, we utilized template summarization with named entities. Template summarization takes less time to run than TextRank but is supervised by the author of the template and script to choose valuable named entities. Thus, it is highly dependent on human intervention to produce reasonable and readable summaries that arent error-prone. As expected, the template summary evaluated well compared to the Gold Standard and the Wikipedia article lead. This result is mainly due to our ability to include named entities we thought were pertinent to the summary. Beyond extractive summaries like TextRank and template summarization, we pursued abstractive summarization using pointer-generator networks and multi-document summarization with pointer-generator networks and maximal marginal relevance. The benefit of using abstractive summarization is that it is more in-line with how humans summarize documents. Pointer-generator networks, however, require GPUs to run properly and a large amount of training data. Luckily, we were able to use a pre-trained network to generate summaries. The pointer-generator network is the centerpiece of our abstractive methods and allowed us to create summaries in the first place. NLP is at an inflection point due to deep learning, and our generated summaries using a state-of-the-art pointer-generator neural network are filled with details about Hurricane Harvey, including damage incurred, the average amount of rainfall, and the locations it affected the most. The summary is also free of grammatical errors. We also use a novel Python library, written by Logan Lebanoff at the University of Central Florida, for multi-document summarization using deep learning to summarize our Hurricane Harvey dataset of 500 articles and the Wikipedia article for Hurricane Harvey. The summary of the Wikipedia article is our final summary and has the highest ROUGE scores that we could attain. ; NSF: IIS-1619028 ; - BDTS_Hurricane_Harvey_final_report.docx: Editable version of the final report - BDTS_Hurricane_Harvey_final_report.pdf: PDF version of the final report - BDTS_Hurricane_Harvey_presentation.pptx: Editable version of the presentation slides - BDTS_Hurricane_Harvey_presentation.pdf: PDF version of the presentation slides Source file in zip: - freq_words.py - Finds the most frequent words in a JSON file that contains a sentences field. Requires a file to be passed through the -f option. - pos_tagging.py - Performs basic part-of-speech tagging on a JSON file that contains a sentences field. Requires a file to be passed through the -f option. - textrank_summarizer.py - Performs TextRank summarization with a JSON file that contains a sentences field. Requires a file to be passed through the -f option. - template_summarizer.py - Performs template summarization with a JSON file that contains a sentences field. Requires a file to be passed through the -f option. - wikipedia_content.py - Extracts content from a Wikipedia page given a topic and formats the information for the pointer-generator network using the make_datafiles.py script. Requires a topic to be given in the -t option and an output directory for make_datafiles.py to read from with the -o option. - make_datafiles.py - Called by "wikipedia_content.py" to convert story files to .bin files. - jusText.py - Used to clean up the large dataset - requirements.txt - Used with Anaconda for installing all of the dependencies. - small_dataset.json - Properly formatted JSON file for use with other files.
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Keyword:
abstractive summarization; big data; big data text summarization; computational linguistics; deep learning; event summarization; extractive summarization; Hurricane Harvey; hurricanes; information extraction; multi-document summarization; natural language processing; neural networks; NLP; pointer-generator network; template filling; text summarization; TextRank; topic summarization
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URL: http://hdl.handle.net/10919/86358
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