ZHAW - I n IT - Social Media Geolocation at V ar D ial 2020 (2020)

Abstract: We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems).

## introduction the 7th workshop on nlp for similar languages, varieties and dialects @xcite introduced a new task on social media geolocation (smg): given a social media post, a system has to predict the latitude and longitude of where it was written. this is an extension to previous evaluation campaigns @xcite @xcite , which focused on dialect identification, assigning a discrete label -usually corresponding to a geographic region -to a piece of text. geolocation prediction allows for a more fine-grained assessment of dialectal varieties without the need to define hard and somewhat arbitrary boundaries within dialect continua. our motivation for participating in the smg shared task was to gain more knowledge about real-world, noisy, digital data. more specifically, we seek to mine written texts for different swiss german dialects and would profit from being able to place them geographically, particularly in the context of our other projects on swiss german. we submitted solutions to all three sub-tasks (see results in section 4) and, in light of our motivation, focused specifically on the swiss sub-task during development. our submissions are based on three different models (see section 3): an svm meta-classifier combining different classifiers based on word and character features for ch (see section 3.4); a single svm with fewer features and no meta-classifer for de-at and bcms (see section 3.6); and a language modelling approach (see section 3.5) which was applied to all subtasks. we furthermore experimented with character-level convolutional neural networks (cnns) (see section 3.7). for all systems, we cluster geolocations to get a number of discrete labels to predict. ## related work the central focus of the evaluation campaign at vardial is to identify dialects of various languages. there have been three previous editions, which laid the basis for dialect identification in swiss german @xcite @xcite . dialect classification is useful for many tasks and applications, e.g. for pos-tagging of dialectal data @xcite , for compilation of german dialect corpora @xcite , or for automatic speech recognition of swiss german. past vardial campaigns have led to the creation of diverse datasets for language and dialect identification, for example: @xcite provide a swiss german dialect data set based on the archi-mob corpus, @xcite present a collection of cuneiform texts derived from a larger open access collection, and @xcite and @xcite created data sets for taiwanese and mandarin chinese. the 2020 smg task is based on social media posts from twitter @xcite and jodel @xcite , annotated with geolocations (see section 3.1). many studies addressed the problem of language and dialect identification, creating a noticeable amount of related work, summarised in the evaluation campaign reports @xcite @xcite and @xcite . a typical approach uses support vector machines (svms) with different feature extraction methods. the use of character language models for language identification has previously been studied by @xcite . over the years various models have been proposed for text-based geolocation prediction @xcite @xcite . as for discretization of geolocations, @xcite propose a hierarchical approach to divide the earth into a grid with different levels of granularity. similarly to @xcite , we use a k-means clustering approach to subdivide the space, which is more data-driven than a grid. our main focus is the ch subtask, where our approach is, from a text classification point of view, most similar to maza, which was proposed at vardial 2017 @xcite . maza uses term frequency (tf) on character n-grams and word unigram features to train several svms. then it uses a random forest meta-classifier with 10-fold cross-validation on the predictions of the svms. we extended this approach and used term frequency-inverse document frequency (tf-idf) on word and on character level. we used an svm as a meta-classifier, and concatenated the output of the base classifiers (see section 3.4). this solution approach was motivated by the fact that we have already applied similar architectures successfully in a wide range of tasks @xcite @xcite , especially in @xcite we established empirically that for (swiss german) dialect recognition tf-idf is better than just tf. for the bcms and de-at subtasks, we used a single svm with word-and character-level tf-idf features (see section 3.6). we also made submissions using a variant of the heli method by @xcite , which we extended with a voting mechanism that takes the centre of the top predicted coordinates in case of low confidence (see section 3.5). ## results in the following, we evaluate the performance of our four systems plus the two simple baseline systems. since we were primarily focusing on the ch subtaks, we present the detailed analysis for these data. later on, we briefly present how our systems performed on the other subtasks. ## conclusion we presented our approach to the vardial 2020 smg shared task, focusing on the submission for the ch subtask. despite the expected noise, caused by people moving between different regions without adjusting their writing, a meta algorithm on top of svms with different n-grams weighted by tf-idf performs impressively well, particularly for switzerland (ch subtask). we achieve the second rank in terms of teams and the third by submissions with a median distance error of only 15.93 km. deep learning approaches combining cnn and k-means also showed interesting results but are still far behind a simple unigram-tf-idf with k-means.