pwc-archive/papers-with-abstracts huggingface dataset:
Graph convolutional networks for exploring authorship hypotheses
Authors: Tom Lippincott
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: General Classification, regression

This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.
Semantics and Homothetic Clustering of Hafez Poetry
Authors: Arya Rahgozar, Diana Inkpen
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Clustering, valid, Word Embeddings

We have created two sets of labels for Hafez (1315-1390) poems, using unsupervised learning. Our labels are the only semantic clustering alternative to the previously existing, hand-labeled, gold-standard classification of Hafez poems, to be used for literary research. We have cross-referenced, measured and analyzed the agreements of our clustering labels with Houman{'}s chronological classes. Our features are based on topic modeling and word embeddings. We also introduced a similarity of similarities{'} features, we called homothetic clustering approach that proved effective, in case of Hafez{'}s small corpus of ghazals2. Although all our experiments showed different clusters when compared with Houman{'}s classes, we think they were valid in their own right to have provided further insights, and have proved useful as a contrasting alternative to Houman{'}s classes. Our homothetic clusterer and its feature design and engineering framework can be used for further semantic analysis of Hafez{'}s poetry and other similar literary research.
Computational Linguistics Applications for Multimedia Services
Authors: Kyeongmin Rim, Kelley Lynch, James Pustejovsky
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We present Computational Linguistics Applications for Multimedia Services (CLAMS), a platform that provides access to computational content analysis tools for archival multimedia material that appear in different media, such as text, audio, image, and video. The primary goal of CLAMS is: (1) to develop an interchange format between multimodal metadata generation tools to ensure interoperability between tools; (2) to provide users with a portable, user-friendly workflow engine to chain selected tools to extract meaningful analyses; and (3) to create a public software development kit (SDK) for developers that eases deployment of analysis tools within the CLAMS platform. CLAMS is designed to help archives and libraries enrich the metadata associated with their mass-digitized multimedia collections, that would otherwise be largely unsearchable.
Correcting Whitespace Errors in Digitized Historical Texts
Authors: S Soni, eep, Lauren Klein, Jacob Eisenstein
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Whitespace errors are common to digitized archives. This paper describes a lightweight unsupervised technique for recovering the original whitespace. Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities. To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States. Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77{\%} recall at a false positive rate of less than 1{\%}, and 91{\%} recall at a false positive rate of less than 3{\%}.
The limits of Spanglish?
Authors: Barbara Bullock, Wally Guzm{\'a}n, Almeida Jacqueline Toribio
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Linguistic code-switching (C-S) is common in oral bilingual vernacular speech. When used in literature, C-S becomes an artistic choice that can mirror the patterns of bilingual interactions. But it can also potentially exceed them. What are the limits of C-S? We model features of C-S in corpora of contemporary U.S. Spanish-English literary and conversational data to analyze why some critics view the {`}Spanglish{'} texts of Ilan Stavans as deviating from a C-S norm.
Sign Clustering and Topic Extraction in Proto-Elamite
Authors: Logan Born, Kate Kelley, Nishant Kambhatla, Carolyn Chen, Anoop Sarkar
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Clustering, Decipherment, Topic Models

We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script. Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs. This demonstrates the utility of these techniques as an aid to manual decipherment.
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
Authors: Qin Dai, Naoya Inoue, Paul Reisert, Ryo Takahashi, Kentaro Inui
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Knowledge Graph Completion, Relation, Relation Extraction

The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.
Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature
Authors: Kritika Agrawal, Aakash Mittal, Vikram Pudi
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Articles

As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental concepts of {``}aim{''}, {''}method{''} and {``}result{''} from scientific articles and use them to construct a knowledge graph. Our algorithm makes use of domain-based word embedding and the bootstrap framework. Our experiments show that our system achieves precision and recall comparable to the state of the art. We also show the domain independence of our algorithm by analyzing the research trends of two distinct communities - computational linguistics and computer vision.
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
Authors: Enrique Noriega-Atala, Zhengzhong Liang, John Bachman, Clayton Morrison, Mihai Surdeanu
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Deep Learning, Reading Comprehension

An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.
Dataset Mention Extraction and Classification
Authors: Animesh Prasad, Chenglei Si, Min-Yen Kan
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Classification, General Classification, zero-shot-classification, Zero-Shot Learning

Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative{'}s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches - exploring the synergy between the tasks - and report the issues with such techniques.
Annotating with Pros and Cons of Technologies in Computer Science Papers
Authors: Hono Shirai, Naoya Inoue, Jun Suzuki, Kentaro Inui
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Sentiment Analysis

This paper explores a task for extracting a technological expression and its pros/cons from computer science papers. We report ongoing efforts on an annotated corpus of pros/cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros/cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.
Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence
Authors: Soham Parikh, Elizabeth Conrad, Oshin Agarwal, Iain Marshall, Byron Wallace, Ani Nenkova
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search. Instead, we propose faceted-search in which a user specifies a condition and then can browse treatments and outcomes that have been evaluated. Choosing from these, they can access randomized control trials (RCTs) describing individual studies. Realizing such a view of the medical evidence requires information extraction techniques to identify the population, interventions, and outcome measures in an RCT. Patients, health practitioners, and biomedical librarians all stand to benefit from such innovation in search of medical evidence. We present an initial prototype of such an interface applied to pre-registered clinical studies. We also discuss pilot studies into the applicability of information extraction methods to allow for similar access to all published trial results.
An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications
Authors: Matthew Magnusson, Laura Dietz
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Word Embeddings

Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP: 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task.
STAC: Science Toolkit Based on Chinese Idiom Knowledge Graph
Authors: Meiling Wang, Min Xiao, Changliang Li, Yu Guo, Zhixin Zhao, Xiaonan Liu
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Cultural Vocal Bursts Intensity Prediction

Chinese idioms (Cheng Yu) have seen five thousand years{'} history and culture of China, meanwhile they contain large number of scientific achievement of ancient China. However, existing Chinese online idiom dictionaries have limited function for scientific exploration. In this paper, we first construct a Chinese idiom knowledge graph by extracting domains and dynasties and associating them with idioms, and based on the idiom knowledge graph, we propose a Science Toolkit for Ancient China (STAC) aiming to support scientific exploration. In the STAC toolkit, idiom navigator helps users explore overall scientific progress from idiom perspective with visualization tools, and idiom card and idiom QA shorten action path and avoid thinking being interrupted while users are reading and writing. The current STAC toolkit is deployed at http://120.92.208.22:7476/demo/{\#}/stac.
Textual and Visual Characteristics of Mathematical Expressions in Scholar Documents
Authors: Vidas Daudaravicius
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Translation

Mathematical expressions (ME) are widely used in scholar documents. In this paper we analyze characteristics of textual and visual MEs characteristics for the image-to-LaTeX translation task. While there are open data-sets of LaTeX files with MEs included it is very complicated to extract these MEs from a document and to compile the list of MEs. Therefore we release a corpus of open-access scholar documents with PDF and JATS-XML parallel files. The MEs in these documents are LaTeX encoded and are document independent. The data contains more than 1.2 million distinct annotated formulae and more than 80 million raw tokens of LaTeX MEs in more than 8 thousand documents. While the variety of textual lengths and visual sizes of MEs are not well defined we found that the task of analyzing MEs in scholar documents can be reduced to the subtask of a particular text length, image width and height bounds, and display MEs can be processed as arrays of partial MEs.
Introduction to Discourse Relation Parsing and Treebanking (DISRPT): 7th Workshop on Rhetorical Structure Theory and Related Formalisms
Authors: Amir Zeldes, Debopam Das, Erick Galani Maziero, Juliano Antonio, Mikel Iruskieta
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Connective Detection

This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop{'}s aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.
Toward Cross-theory Discourse Relation Annotation
Authors: Peter Bourgonje, Olha Zolotarenko
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Articles, Implicit Relations, Relation

In this exploratory study, we attempt to automatically induce PDTB-style relations from RST trees. We work with a German corpus of news commentary articles, annotated for RST trees and explicit PDTB-style relations and we focus on inducing the implicit relations in an automated way. Preliminary results look promising as a high-precision (but low-recall) way of finding implicit relations where there is no shallow structure annotated at all, but mapping proves more difficult in cases where EDUs and relation arguments overlap, yet do not seem to signal the same relation.
Nuclearity in RST and signals of coherence relations
Authors: Debopam Das
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We investigate the relationship between the notion of nuclearity as proposed in Rhetorical Structure Theory (RST) and the signalling of coherence relations. RST relations are categorized as either mononuclear (comprising a nucleus and a satellite span) or multinuclear (comprising two or more nuclei spans). We examine how mononuclear relations (e.g., Antithesis, Condition) and multinuclear relations (e.g., Contrast, List) are indicated by relational signals, more particularly by discourse markers (e.g., because, however, if, therefore). We conduct a corpus study, examining the distribution of either type of relations in the RST Discourse Treebank (Carlson et al., 2002) and the distribution of discourse markers for those relations in the RST Signalling Corpus (Das et al., 2015). Our results show that discourse markers are used more often to signal multinuclear relations than mononuclear relations. The findings also suggest a complex relationship between the relation types and syntactic categories of discourse markers (subordinating and coordinating conjunctions).
The Rhetorical Structure of Attribution
Authors: Andrew Potter
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Relation

The relational status of Attribution in Rhetorical Structure Theory has been a matter of ongoing debate. Although several researchers have weighed in on the topic, and although numerous studies have relied upon attributional structures for their analyses, nothing approaching consensus has emerged. This paper identifies three basic issues that must be resolved to determine the relational status of attributions. These are identified as the Discourse Units Issue, the Nuclearity Issue, and the Relation Identification Issue. These three issues are analyzed from the perspective of classical RST. A finding of this analysis is that the nuclearity and the relational identification of attribution structures are shown to depend on the writer{'}s intended effect, such that attributional relations cannot be considered as a single relation, but rather as attributional instances of other RST relations.
Annotating Shallow Discourse Relations in Twitter Conversations
Authors: Tatjana Scheffler, Berfin Akta{\c{s}}, Debopam Das, Manfred Stede
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We introduce our pilot study applying PDTB-style annotation to Twitter conversations. Lexically grounded coherence annotation for Twitter threads will enable detailed investigations of the discourse structure of conversations on social media. Here, we present our corpus of 185 threads and annotation, including an inter-annotator agreement study. We discuss our observations as to how Twitter discourses differ from written news text wrt. discourse connectives and relations. We confirm our hypothesis that discourse relations in written social media conversations are expressed differently than in (news) text. We find that in Twitter, connective arguments frequently are not full syntactic clauses, and that a few general connectives expressing EXPANSION and CONTINGENCY make up the majority of the explicit relations in our data.
A Discourse Signal Annotation System for RST Trees
Authors: Luke Gessler, Yang Liu, Amir Zeldes
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

This paper presents a new system for open-ended discourse relation signal annotation in the framework of Rhetorical Structure Theory (RST), implemented on top of an online tool for RST annotation. We discuss existing projects annotating textual signals of discourse relations, which have so far not allowed simultaneously structuring and annotating words signaling hierarchical discourse trees, and demonstrate the design and applications of our interface by extending existing RST annotations in the freely available GUM corpus.
EusDisParser: improving an under-resourced discourse parser with cross-lingual data
Authors: Mikel Iruskieta, Chlo{\'e} Braud
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Multilingual Word Embeddings, Word Embeddings

Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.
Towards the Data-driven System for Rhetorical Parsing of Russian Texts
Authors: Artem Shelmanov, Dina Pisarevskaya, Elena Chistova, Svetlana Toldova, Maria Kobozeva, Ivan Smirnov
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: BIG-bench Machine Learning, Classification, General Classification, Relation, Relation Classification

Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank {--} first Russian corpus annotated within RST framework {--} are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 {\mbox{$\pm$}} 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.