Spain

This poster accompanies the publication of the EncycNet corpus, a novel XML/TEI annotated corpus of 22 historical German encyclopedias from the early 18th to early 20th century (over 50 million word tokens). While some previous encyclopedia projects have suffered from restrictive or unclear licensing, or paywalled or no longer functional web interfaces, we share the EncycNet corpus under the CC-BY license and long term archiving at Zenodo. This poster describes our suggested methodology for TEI annotation, namely how the TEI Lex-0 standard may be modified with additional guidelines for the annotation of historical encyclopedias. We suggest some possible use cases for the EncycNet corpus, including linguistic, historical, and literary research, and present our plan for future work, including the addition of further semantic annotation, alignment of encyclopedia entries based on semantic web (GermaNet and DBpedia), and the development of the corpus as a knowledge graph. As the digitization and annotation of historical encyclopedias is always moving forward, our methodological choices may inform similar projects. The EncycNet corpus is available at: http://dx.doi.org/10.5281/zenodo.4039569

COLING 2020

Twenty-two Historical Encyclopedias Encoded in TEI: a New Resource for the Digital Humanities

encyclopedia

historical

corpus

german

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.


Geometric Deep Learning Models for Linking Character Names in Novels

We describe OuPoCo (l’Ouvroir de Poésie Combinatoire), a system producing new sonnets by recombining verses from existing sonnets, following an idea that Queneau described in his book Cent Mille Milliards de poèmes, Gallimard, 1961. We propose to demonstrate different outputs of our implementation (a Web site, a Twitter bot and a specifically developed device, called La Boîte à poésie) based on a corpus of 19th century French poetry. Our goal is to make people interested in poetry again, by giving access to automatically produced sonnets through original and entertaining channels and devices.

Sonnet Combinatorics with OuPoCo

Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them. Descriptions of smells in text, or as we call them `smell experiences', offer a window into these factors, but they must first be identified. To the best of our knowledge, no tool exists to extract references to smell experiences from text. In this paper, we present two variations on a semi-supervised approach to identify smell experiences in English literature. The combined set of patterns from both implementations offer significantly better performance than a keyword-based baseline.

Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels

TL-Explorer is a digital humanities tool for mapping and analyzing translated literature, encompassing the World Map and the Translation Dashboard. The World Map displays collected literature of different languages, locations, and cultures and establishes the foundation for a variety of further analysis. It is comprised of three global maps for spatial and temporal interpretation. A further investigation into an individual node on the World Map — representing one edition or translation — leads to the Translation Dashboard. Collected translations are processed in order to build multilingual parallel corpora for a large number of under-resourced languages as well as to highlight the transnational circulation of knowledge. 

Our first rendition of TL-Explorer was conducted on the well-traveled American novel, Adventures of Huckleberry Finn, by Mark Twain. The maps currently chronicle nearly 400 translations of this novel and the dashboard supports over 30 collected translations. However, the TL-Explore is easily extended to other works of literature and is not limited to type of texts, such as academic manuscripts or constitutional documents to name a few.

TL-Explorer: A Digital Humanities Tool for Mapping and Analyzing Translated Literature

The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.

Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan

We present a classifier for tweets that mention medications or supplements, based on a pretrained transformer. We developed such a system for our participation in Subtask 1 of the Social Media Mining for Health Application workshop, which featured an extremely unbalanced dataset. The model showed promising results, with an F1of 0.8 (task mean: 0.66).

RoBERTa for detecting medications on Twitter

This poster describes the method developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model’s F1-Score of 0.56 on the test dataset was 10% better than the mean of the F1-Score of the best submissions in the task.

RoBERTa and Rules Adverse drug reaction detection in Twitter

This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2% of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.

pre-trained sentence encoders

This work describes our participation in the Social Media Mining for Health Application(SMM4H 2020) Challenge Track 2 for identifying tweets containing Adverse Effects (AEs). Our system uses Convolutional Neural Networks. We explore downsampling, oversampling, and adjusting the class weights to account for the imbalanced nature of the dataset. Our results showed downsampling outperformed oversampling and adjusting the class weights on the test set however all three obtained similar results on the development set.

Identifying adverse effects in English tweets for unbalanced data

This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2. Our approach utilizes multilingual sentence embeddings (sentence-BERT) for representing tweets and Bayesian hyperparameter optimization of sample weighting parameter for counterbalancing high class imbalance.

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