pwc-archive/papers-with-abstracts huggingface dataset:
SpeechTrans@SMM4H’20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications
Authors: Mohamed Lichouri, Mourad Abbas
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12

This paper describes our system developed for automatically classifying tweets that mention medications. We used the Decision Tree classifier for this task. We have shown that using some elementary preprocessing steps and TF-IDF n-grams led to acceptable classifier performance. Indeed, the F1-score recorded was 74.58% in the development phase and 63.70% in the test phase.
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture
Authors: Saichethan Reddy
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Classification, Multi-class Classification

This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7% over our single view model for the fifth task. Code of our proposed framework is made available on Github
Identification of Medication Tweets Using Domain-specific Pre-trained Language Models
Authors: Yandrapati Prakash Babu, Rajagopal Eswari
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12

In this paper, we present our approach for task1 of SMM4H 2020. This task involves automatic classification of tweets mentioning medication or dietary supplements. For this task, we experiment with pre-trained models like Biomedical RoBERTa, Clinical BERT and Biomedical BERT. Our approach achieves F1-score of 73.56%.
Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees
Authors: Lung-Hao Lee, Po-Han Chen, Hao-Chuan Kao, Ting-Chun Hung, Po-Lei Lee, Kuo-Kai Shyu
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Data Augmentation

This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.
LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets
Authors: Ludovic Tanguy, Lydia-Mai Ho-Dac, Cécile Fabre, Roxane Bois, Touati Mohamed Yacine Haddad, Claire Ibarboure, Marie Joyau, François Le moal, Jade Moiilic, Laura Roudaut, Mathilde Simounet, Irena Stankovic, Mickaela Vandewaetere
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: regression, Task 2

This paper describes our participation to the SMM4H shared task 2. We designed a rule-based classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French, and is based on a number of specific word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.
Sentence Classification with Imbalanced Data for Health Applications
Authors: Farhana Ferdousi Liza
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Classification, Sentence, Sentence Classification, Task 2

Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for machine learning algorithms. Learning algorithms typically designed to optimize the overall accuracy without considering the relative distribution of each class. Thus, imbalanced class distribution is problematic as learning algorithms have low predictive accuracy for the infrequent class. Moreover, social media represents natural linguistic variation in creative language expressions. In this paper, we have used a combination of data balancing and neural language representation techniques to address the challenges. Specifically, we participated the shared tasks 1, 2 (all languages), 4, and 3 (only the span detection, no normalization was attempted) in Social Media Mining for Health applications (SMM4H) 2020 (Klein et al., 2020). The results show that with the proposed methodology recall scores are better than the precision scores for the shared tasks. The recall score is also better compared to the mean score of the total submissions. However, the F1-score is worse than the mean score except for task 2 (French).
HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
Authors: Xiaoyu Zhao, Ying Xiong, Buzhou Tang
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Classification, Task 2

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first task is automatic classification of tweets that mention medications and the second task is automatic classification of tweets in English that report adverse effects. The system we propose for these tasks is based on bidirectional encoder representations from transformers (BERT) incorporating with knowledge graph and retrieving evidence from online information. Our system achieves an F1 of 0.7553 in task 1 and an F1 of 0.5455 in task 2.
Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders
Authors: Laiba Mehnaz
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Classification, Sentence, text-classification, Text Classification

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.
Approaching SMM4H 2020 with Ensembles of BERT Flavours
Authors: George-Andrei Dima, Andrei-Marius Avram, Dumitru-Clementin Cercel
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Task 2

This paper describes our solutions submitted to the Social Media Mining for Health Applications (#SMM4H) Shared Task 2020. We participated in the following tasks: Task 1 aimed at classifying if a tweet reports medications or not, Task 2 (only for the English dataset) aimed at discriminating if a tweet mentions adverse effects or not, and Task 5 aimed at recognizing if a tweet mentions birth defects or not. Our work focused on studying different neural network architectures based on various flavors of bidirectional Transformers (i.e., BERT), in the context of the previously mentioned classification tasks. For Task 1, we achieved an F1-score (70.5%) above the mean performance of the best scores made by all teams, whereas for Task 2, we obtained an F1-score of 37%. Also, we achieved a micro-averaged F1-score of 62% for Task 5.
NLP@VCU: Identifying Adverse Effects in English Tweets for Unbalanced Data
Authors: Darshini Mahendran, Cora Lewis, Bridget McInnes
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12

This paper 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.
Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter
Authors: Oguzhan Gencoglu
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Bayesian Optimization, Hyperparameter Optimization, Sentence, Sentence Embeddings, Task 2

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.
Sentence Contextual Encoder with BERT and BiLSTM for Automatic Classification with Imbalanced Medication Tweets
Authors: Olanrewaju Tahir Aduragba, Jialin Yu, Gautham Senthilnathan, Alexandra Crsitea
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Sentence

This paper details the system description and approach used by our team for the SMM4H 2020 competition, Task 1. Task 1 targets the automatic classification of tweets that mention medication. We adapted the standard BERT pretrain-then-fine-tune approach to include an intermediate training stage with a biLSTM architecture neural network acting as a further fine-tuning stage. We were inspired by the effectiveness of within-task further pre-training and sentence encoders. We show that this approach works well for a highly imbalanced dataset. In this case, the positive class is only 0.2% of the entire dataset. Our model performed better in both F1 and precision scores compared to the mean score for all participants in the competition and had a competitive recall score.
CLaC at SMM4H 2020: Birth Defect Mention Detection
Authors: Parsa Bagherzadeh, Sabine Bergler
Date: 2020-12-01 00:00:00
Proceeding: SMM4H (COLING) 2020 12
Tasks: Word Embeddings

For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.
The Corpora They Are a-Changing: a Case Study in Italian Newspapers
Authors: Pierpaolo Basile, Annalina Caputo, Tommaso Caselli, Pierluigi Cassotti, Rossella Varvara
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8
Tasks: Optical Character Recognition (OCR)

The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks. Benchmark datasets, however, are intimately tied to the corpus used for their creation questioning their reliability as well as the robustness of automatic methods. This contribution investigates these aspects showing the impact of unforeseen social and cultural dimensions. We also identify a set of additional issues (OCR quality, named entities) that impact the performance of the automatic methods, especially when used to discover LSC.
Linguistic change and historical periodization of Old Literary Finnish
Authors: Niko Partanen, Khalid Alnajjar, Mika Hämäläinen, Jack Rueter
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8
Tasks: Lemmatization, Word Embeddings

In this study, we have normalized and lemmatized an Old Literary Finnish corpus using a lemmatization model trained on texts from Agricola. We analyse the error types that occur and appear in different decades, and use word error rate (WER) and different error types as a proxy for measuring linguistic innovation and change. We show that the proposed approach works, and the errors are connected to accumulating changes and innovations, which also results in a continuous decrease in the accuracy of the model. The described error types also guide further work in improving these models, and document the currently observed issues. We also have trained word embeddings for four centuries of lemmatized Old Literary Finnish, which are available on Zenodo.
The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek
Authors: Alek Keersmaekers
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8

This paper describes the GLAUx project (“the Greek Language Automated”), an ongoing effort to develop a large long-term diachronic corpus of Greek, covering sixteen centuries of literary and non-literary material annotated with NLP methods. After providing an overview of related corpus projects and discussing the general architecture of the corpus, it zooms in on a number of larger methodological issues in the design of historical corpora. These include the encoding of textual variants, handling extralinguistic variation and annotating linguistic ambiguity. Finally, the long- and short-term perspectives of this project are discussed.
Bhāṣācitra: Visualising the dialect geography of South Asia
Authors: Aryaman Arora, Adam Farris, Gopalakrishnan R, Samopriya Basu
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8

We present Bhāṣācitra, a dialect mapping system for South Asia built on a database of linguistic studies of languages of the region annotated for topic and location data. We analyse language coverage and look towards applications to typology by visualising example datasets. The application is not only meant to be useful for feature mapping, but also serves as a new kind of interactive bibliography for linguists of South Asian languages.
Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks
Authors: Tim Reke, Robert Schwanhold, Ralf Krestel
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8

Languages evolve over time and the meaning of words can shift. Furthermore, individual words can have multiple senses. However, existing language models often only reflect one word sense per word and do not reflect semantic changes over time. While there are language models that can either model semantic change of words or multiple word senses, none of them cover both aspects simultaneously. We propose a novel force-directed graph layout algorithm to draw a network of frequently co-occurring words. In this way, we are able to use the drawn graph to visualize the evolution of word senses. In addition, we hope that jointly modeling semantic change and multiple senses of words results in improvements for the individual tasks.
Tracking Semantic Change in Cognate Sets for English and Romance Languages
Authors: Ana Sabina Uban, Alina Maria Cristea, Anca Dinu, Liviu P. Dinu, Simona Georgescu, Laurentiu Zoicas
Date: 2021-08-01 00:00:00
Proceeding: ACL (LChange) 2021 8
Tasks: Word Embeddings

Semantic divergence in related languages is a key concern of historical linguistics. We cross-linguistically investigate the semantic divergence of cognate pairs in English and Romance languages, by means of word embeddings. To this end, we introduce a new curated dataset of cognates in all pairs of those languages. We describe the types of errors that occurred during the automated cognate identification process and manually correct them. Additionally, we label the English cognates according to their etymology, separating them into two groups: old borrowings and recent borrowings. On this curated dataset, we analyse word properties such as frequency and polysemy, and the distribution of similarity scores between cognate sets in different languages. We automatically identify different clusters of English cognates, setting a new direction of research in cognates, borrowings and possibly false friends analysis in related languages.
An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition
Authors: Takuya Komada, Takashi Inui
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11
Tasks: Image Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Retrieval

In recent years, previous studies have used visual information in named entity recognition (NER) for social media posts with attached images. However, these methods can only be applied to documents with attached images. In this paper, we propose a NER method that can use element-wise visual information for any documents by using image data corresponding to each word in the document. The proposed method obtains element-wise image data using an image retrieval engine, to be used as extra features in the neural NER model. Experimental results on the standard Japanese NER dataset show that the proposed method achieves a higher F1 value (89.67%) than a baseline method, demonstrating the effectiveness of using element-wise visual information.
BERT-based Spatial Information Extraction
Authors: Hyeong Jin Shin, Jeong Yeon Park, Dae Bum Yuk, Jae Sung Lee
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11
Tasks: Relation, Relation Extraction

Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction. The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4% point increase in spatial element extraction and an 8.2% point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).
A Cognitively Motivated Approach to Spatial Information Extraction
Authors: Chao Xu, Emmanuelle-Anna Dietz Saldanha, Dagmar Gromann, Beihai Zhou
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11

Automatic extraction of spatial information from natural language can boost human-centered applications that rely on spatial dynamics. The field of cognitive linguistics has provided theories and cognitive models to address this task. Yet, existing solutions tend to focus on specific word classes, subject areas, or machine learning techniques that cannot provide cognitively plausible explanations for their decisions. We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language. The proposed rule-based and explainable approach contributes constructions and preposition schemas and outperforms previous solutions on the CLEF-2017 standard dataset.
They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies
Authors: Alberto Testoni, Claudio Greco, Tobias Bianchi, Mauricio Mazuecos, Agata Marcante, Luciana Benotti, Raffaella Bernardi
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11
Tasks: All

In this paper, we study the grounding skills required to answer spatial questions asked by humans while playing the GuessWhat?! game. We propose a classification for spatial questions dividing them into absolute, relational, and group questions. We build a new answerer model based on the LXMERT multimodal transformer and we compare a baseline with and without visual features of the scene. We are interested in studying how the attention mechanisms of LXMERT are used to answer spatial questions since they require putting attention on more than one region simultaneously and spotting the relation holding among them. We show that our proposed model outperforms the baseline by a large extent (9.70% on spatial questions and 6.27% overall). By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
Categorisation, Typicality & Object-Specific Features in Spatial Referring Expressions
Authors: Adam Richard-Bollans, Anthony Cohn, Lucía Gómez Álvarez
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11
Tasks: Object

Various accounts of cognition and semantic representations have highlighted that, for some concepts, different factors may influence category and typicality judgements. In particular, some features may be more salient in categorisation tasks while other features are more salient when assessing typicality. In this paper we explore the extent to which this is the case for English spatial prepositions and discuss the implications for pragmatic strategies and semantic models. We hypothesise that object-specific features — related to object properties and affordances — are more salient in categorisation, while geometric and physical relationships between objects are more salient in typicality judgements. In order to test this hypothesis we conducted a study using virtual environments to collect both category and typicality judgements in 3D scenes. Based on the collected data we cannot verify the hypothesis and conclude that object-specific features appear to be salient in both category and typicality judgements, further evidencing the need to include these types of features in semantic models.
A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports
Authors: Surabhi Datta, Kirk Roberts
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11

Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical devices with respect to some anatomical structures. As the expressions result from the mental visualization of the radiologist’s interpretations, they are varied and complex. The focus of this work is to automatically identify the spatial expression terms from three different radiology sub-domains. We propose a hybrid deep learning-based NLP method that includes – 1) generating a set of candidate spatial triggers by exact match with the known trigger terms from the training data, 2) applying domain-specific constraints to filter the candidate triggers, and 3) utilizing a BERT-based classifier to predict whether a candidate trigger is a true spatial trigger or not. The results are promising, with an improvement of 24 points in the average F1 measure compared to a standard BERT-based sequence labeler.
Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View
Authors: Harsh Mehta, Yoav Artzi, Jason Baldridge, Eugene Ie, Piotr Mirowski
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SpLU) 2020 11
Tasks: Vision and Language Navigation

The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in (Chen et al., 2019) and show that the panoramas we have added to StreetLearn support both Touchdown tasks and can be used effectively for further research and comparison.