Papers by Steve Skiena
Chapter Ordering in Novels (2022.emnlp-main)
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| Challenge: | a major challenge in research on long-form narrative texts is the cost of annotation . authors propose a new task that reconstructs the original order of chapters in novels without the need for human annotation. |
| Approach: | They propose a task that reconstructs the original order of chapters in novels given a random permutation of the text. |
| Outcome: | The proposed task yields a Spearman correlation of 0.59 on the novel and challenging task, substantially above baseline. |
Chapter Captor: Text Segmentation in Novels (2020.emnlp-main)
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| Challenge: | Using a hybrid approach, we identify chapter boundaries in novels . chapter boundaries are typically denoted by formatting conventions such as page breaks, white-space, chapter numbers, and titles. |
| Approach: | They build a project Gutenberg data set of 9,126 English novels to analyze chapter boundaries . they use neural inference and rule matching to recognize chapter title headers . |
| Outcome: | The proposed method achieves an F1 score of 0.77 on the segmentation task . the annotated data reveal interesting historical trends in the chapter structure of novels . |
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts (2021.findings-emnlp)
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| Challenge: | a large amount of work is required to clean digitized books for NLP analysis because of errors in the scanned text and duplicate volumes in the corpora. |
| Approach: | They propose methods to handle optical character recognition errors in scanned texts . they identify the canonical version for each of 17,136 repeatedly-scanned books . |
| Outcome: | The proposed method corrects over six times as many errors as it introduces, the authors show . the authors evaluate a collection of 19,347 texts from the Gutenberg dataset and 96,635 from the HathiTrust Library . |
What time is it? Temporal Analysis of Novels (2020.emnlp-main)
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| Challenge: | a novel based on the flow of time provides a framework for understanding the text . a computational approach to annotate a book's lines with wall clock times is needed to understand the flow through time. |
| Approach: | They propose to annotate each line of a book with wall clock times . they use a data set of hourly time phrases from 52,183 fictional books . |
| Outcome: | The proposed method improves upon baselines by over two hours and can partition a book into segments that correspond to a particular time-of-day. |
Multi-view Models for Political Ideology Detection of News Articles (D18-1)
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| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |
The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets (D19-1)
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| Challenge: | characterization scores are associated with popularity of a given short text, but are not always representative of the source. |
| Approach: | They use a dataset of tweets from 15 celebrities to quantify the extent to which a given short text is characteristic of a specific person. |
| Outcome: | The proposed model shows a statistically significant correlation between characterization scores and popularity of the associated texts for 13 of the 15 celebrities in the study. |
Simple Neologism Based Domain Independent Models to Predict Year of Authorship (C18-1)
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| Challenge: | Using domain independent models, we date documents based only on neologism usage patterns . nasa models use only 200 input features, compared to state of the art models using 200K features. |
| Approach: | They propose domain independent models to date documents based only on neologism usage patterns. |
| Outcome: | The proposed models can generalize to various domains like News, Fiction, and Non-Fiction with competitive performance. |