pwc-archive/papers-with-abstracts huggingface dataset:
Improving Named Entity Linking Corpora Quality
Authors: Albert Weichselbraun, Adrian M.P. Brasoveanu, Philipp Kuntschik, Lyndon J.B. Nixon
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Benchmarking, Entity Linking

Gold standard corpora and competitive evaluations play a key role in benchmarking named entity linking (NEL) performance and driving the development of more sophisticated NEL systems. The quality of the used corpora and the used evaluation metrics are crucial in this process. We, therefore, assess the quality of three popular evaluation corpora, identifying four major issues which affect these gold standards: (i) the use of different annotation styles, (ii) incorrect and missing annotations, (iii) Knowledge Base evolution, (iv) and differences in annotating co-occurrences. This paper addresses these issues by formalizing NEL annotations and corpus versioning which allows standardizing corpus creation, supports corpus evolution, and paves the way for the use of lenses to automatically transform between different corpus configurations. In addition, the use of clearly defined scoring rules and evaluation metrics ensures a better comparability of evaluation results.
Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case
Authors: Martin Wolf, Volha Petukhova, Dietrich Klakow
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Question Answering, Retrieval, Term Extraction

The processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions are modelled as doctor-patient Question Answering sessions within a pre-operative patient education scenario where the system addresses patient{'}s information needs explaining medical events and procedures. This implies an accurate medical information extraction from and reasoning with available medical knowledge and large amounts of unstructured multilingual online data. Bridging the gap between medical knowledge and data, we explore a language-agnostic approach to medical concepts mining from the standard terminologies, and the data-driven collection of the corresponding seed terms in a distant supervision setting for German. Experimenting with different terminologies, features and term matching strategies, we achieved a promising F-score of 0.91 on the medical term extraction task. The concepts and terms are used to search and retrieve definitions from the verified online free resources. The proof-of-concept definition retrieval system is designed and evaluated showing promising results, acceptable by humans in 92{\%} of cases.
A Survey of the Perceived Text Adaptation Needs of Adults with Autism
Authors: Victoria Yaneva, Constantin Orasan, Le An Ha, Natalia Ponomareva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Survey

NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups. One such group are adults with high-functioning autism, who are usually able to read long sentences and comprehend difficult words but whose comprehension may be impeded by other linguistic constructions. This is especially challenging for real-world user-generated texts such as product reviews, which cannot be controlled editorially and are thus a particularly good applcation for automatic text adaptation systems. In this paper we present a mixed-methods survey conducted with 24 adult web-users diagnosed with autism and an age-matched control group of 33 neurotypical participants. The aim of the survey was to identify whether the group with autism experienced any barriers when reading online reviews, what these potential barriers were, and what NLP methods would be best suited to improve the accessibility of online reviews for people with autism. The group with autism consistently reported significantly greater difficulties with understanding online product reviews compared to the control group and identified issues related to text length, poor topic organisation, and the use of irony and sarcasm.
An Open, Extendible, and Fast Turkish Morphological Analyzer
Authors: Olcay Taner Y{\i}ld{\i}z, Beg{\"u}m Avar, G{\"o}khan Ercan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Morphological Analysis

In this paper, we present a two-level morphological analyzer for Turkish. The morphological analyzer consists of five main components: finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache. We use Java language to implement finite state machine logic and rule engine, Xml language to describe the finite state transducer rules of the Turkish language, which makes the morphological analyzer both easily extendible and easily applicable to other languages. Empowered with the comprehensiveness of a lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer presents one of the most reliable analyzers produced so far. The analyzer is compared with Turkish morphological analyzers in the literature. By using LRU cache and a trie data structure, the system can analyze 100,000 words per second, which enables users to analyze huge corpora in a few hours.
Turkish Tweet Classification with Transformer Encoder
Authors: At{\i}f Emre Y{\"u}ksel, Ya{\c{s}}ar Alim T{\"u}rkmen, Arzucan {\"O}zg{\"u}r, Berna Alt{\i}nel
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification, Morphological Analysis, text-classification, Text Classification

Short-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on their topics. Social media jargon and the agglutinative structure of the Turkish language makes this classification task even harder. As far as we know, this is the first study that uses a Transformer Encoder for short text classification in Turkish. The model is trained in a weakly supervised manner, where the training data set has been labeled automatically. Our results on the test set, which has been manually labeled, show that performing morphological analysis improves the classification performance of the traditional machine learning algorithms Random Forest, Naive Bayes, and Support Vector Machines. Still, the proposed approach achieves an F-score of 89.3 {\%} outperforming those algorithms by at least 5 points.
Multilingual Dynamic Topic Model
Authors: Elaine Zosa, Mark Granroth-Wilding
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, model, Time Series, Time Series Analysis, Topic Models

Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data.Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture cross-lingual topics that evolve across time. We present results of this model on a parallel German-English corpus of news articles and a comparable corpus of Finnish and Swedish news articles. We demonstrate the capability of ML-DTM to track significant events related to a topic and show that it finds distinct topics and performs as well as existing multilingual topic models in aligning cross-lingual topics.
A Wide-Coverage Context-Free Grammar for Icelandic and an Accompanying Parsing System
Authors: Vilhj{\'a}lmur {\TH}orsteinsson, Hulda {\'O}lad{\'o}ttir, Hrafn Loftsson
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles

We present an open-source, wide-coverage context-free grammar (CFG) for Icelandic, and an accompanying parsing system. The grammar has over 5,600 nonterminals, 4,600 terminals and 19,000 productions in fully expanded form, with feature agreement constraints for case, gender, number and person. The parsing system consists of an enhanced Earley-based parser and a mechanism to select best-scoring parse trees from shared packed parse forests. Our parsing system is able to parse about 90{\%} of all sentences in articles published on the main Icelandic news websites. Preliminary evaluation with evalb shows an F-measure of 70.72{\%} on parsed sentences. Our system demonstrates that parsing a morphologically rich language using a wide-coverage CFG can be practical.
Normalization of Kazakh Texts
Authors: Assina Abdussaitova, Alina Amangeldiyeva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning

Kazakh language, like other agglutinative languages, has specific difficulties on both recognition of wrong words and generation the corrections for misspelt words. The main goal of this work is to develop a better algorithm for the normalization of Kazakh texts based on traditional and Machine Learning methods, as well as the new approach which is also considered in this paper. The procedure of election among methods of normalization has been conducted in a manner of comparative analysis. The results of the comparative analysis turned up successful and are shown in details.
Classification Approaches to Identify Informative Tweets
Authors: Piush Aggarwal
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification

Social media platforms have become prime forums for reporting news, with users sharing what they saw, heard or read on social media. News from social media is potentially useful for various stakeholders including aid organizations, news agencies, and individuals. However, social media also contains a vast amount of non-news content. For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news. In this paper, we tackle the challenge of classifying a social post as news or not. To this end, we provide a new manually annotated dataset containing 2,992 tweets from 5 different topical categories. Unlike earlier datasets, it includes postings posted by personal users who do not promote a business or a product and are not affiliated with any organization. We also investigate various baseline systems and evaluate their performance on the newly generated dataset. Our results show that the best classifiers are the SVM and BERT models.
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English
Authors: Rachel Dorn
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, speech-recognition, Speech Recognition

African American Vernacular English (AAVE) is a widely-spoken dialect of English, yet it is under-represented in major speech corpora. As a result, speakers of this dialect are often misunderstood by NLP applications. This study explores the effect on transcription accuracy of an automatic voice recognition system when AAVE data is used. Models trained on AAVE data and on Standard American English data were compared to a baseline model trained on a combination of the two dialects. The accuracy for both dialect-specific models was significantly higher than the baseline model, with the AAVE model showing over 18{\%} improvement. By isolating the effect of having AAVE speakers in the training data, this study highlights the importance of increasing diversity in the field of natural language processing.
Multilingual Language Models for Named Entity Recognition in German and English
Authors: Antonia Baumann
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Specificity

We assess the language specificity of recent language models by exploring the potential of a multilingual language model. In particular, we evaluate Google{'}s multilingual BERT (mBERT) model on Named Entity Recognition (NER) in German and English. We expand the work on language model fine-tuning by Howard and Ruder (2018), applying it to the BERT architecture. We successfully reproduce the NER results published by Devlin et al. (2019).Our results show that the multilingual language model generalises well for NER in the chosen languages, matching the native model in English and comparing well with recent approaches for German. However, it does not benefit from the added fine-tuning methods.
Parts of Speech Tagging for Kannada
Authors: Swaroop L R, Rakshith Gowda G S, Sourabh U, Shriram Hegde
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: POS, POS Tagging, Sentence, TAG

Parts of speech (POS) tagging is the process of assigning the part of speech tag to each and every word in a sentence. In this paper, we have presented POS tagger for Kannada, a low resource south Asian language, using Condition Random Fields. POS tagger developed in the work uses novel features native to Kannada language. The novel features include Sandhi splitting, where a compound word is broken down into two or more meaningful constituent words. The proposed model is trained and tested on the tagged dataset which contains 21 thousand sentences and achieves a highest accuracy of 94.56{\%}.
Cross-Lingual Coreference: The Case of Bulgarian and English
Authors: Zara Kancheva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: coreference-resolution, Coreference Resolution

The paper presents several common approaches towards cross- and multi-lingual coreference resolution in a search of the most effective practices to be applied within the work on Bulgarian-English manual coreference annotation of a short story. The work aims at outlining the typology of the differences in the annotated parallel texts. The results of the research prove to be comparable with the tendencies observed in similar works on other Slavic languages and show surprising differences between the types of markables and their frequency in Bulgarian and English.
Towards Accurate Text Verbalization for ASR Based on Audio Alignment
Authors: Diana Geneva, Georgi Shopov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Verbalization of non-lexical linguistic units plays an important role in language modeling for automatic speech recognition systems. Most verbalization methods require valuable resources such as ground truth, large training corpus and expert knowledge which are often unavailable. On the other hand a considerable amount of audio data along with its transcribed text are freely available on the Internet and could be utilized for the task of verbalization. This paper presents a methodology for accurate verbalization of audio transcriptions based on phone-level alignment between the transcriptions and their corresponding audio recordings. Comparing this approach to a more general rule-based verbalization method shows a significant improvement in ASR recognition of non-lexical units. In the process of evaluating this approach we also expose the indirect influence of verbalization accuracy on the quality of acoustic models trained on automatically derived speech corpora.
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC
Authors: Iva Marinova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, POS, POS Tagging, Word Embeddings

This paper reports on experiments with different stacks of word embeddings and evaluation of their usefulness for Bulgarian downstream tasks such as Named Entity Recognition and Classification (NERC) and Part-of-speech (POS) Tagging. Word embeddings stay in the core of the development of NLP, with several key language models being created over the last two years like FastText (CITATION), ElMo (CITATION), BERT (CITATION) and Flair (CITATION). Stacking or combining different word embeddings is another technique used in this paper and still not reported for Bulgarian NERC. Well-established architecture is used for the sequence tagging task such as BI-LSTM-CRF, and different pre-trained language models are combined in the embedding layer to decide which combination of them scores better.
Overview on NLP Techniques for Content-based Recommender Systems for Books
Authors: Melania Berbatova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Recommendation Systems

Recommender systems are an essential part of today{'}s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads{'} website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.
Corpora and Processing Tools for Non-standard Contemporary and Diachronic Balkan Slavic
Authors: Teodora Vukovic, Nora Muheim, Olivier Winist{\"o}rfer, Ivan {\v{S}}imko, Anastasia Makarova, Sanja Bradjan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Lemmatization, POS

The paper describes three corpora of different varieties of BS that are currently being developed with the goal of providing data for the analysis of the diatopic and diachronic variation in non-standard Balkan Slavic. The corpora includes spoken materials from Torlak, Macedonian dialects, as well as the manuscripts of pre-standardized Bulgarian. Apart from the texts, tools for PoS annotation and lemmatization for all varieties are being created, as well as syntactic parsing for Torlak and Bulgarian varieties. The corpora are built using a unified methodology, relying on the pest practices and state-of-the-art methods from the field. The uniform methodology allows the contrastive analysis of the data from different varieties. The corpora under construction can be considered a crucial contribution to the linguistic research on the languages in the Balkans as they provide the lacking data needed for the studies of linguistic variation in the Balkan Slavic, and enable the comparison of the said varieties with other neighbouring languages.
Question Answering Systems Approaches and Challenges
Authors: Reem Alqifari
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Question Answering

Question answering (QA) systems permit the user to ask a question using natural language, and the system provides a concise and correct answer. QA systems can be implemented for different types of datasets, structured or unstructured. In this paper, some of the recent studies will be reviewed and the limitations will be discussed. Consequently, the current issues are analyzed with the proposed solutions.
Adding Linguistic Knowledge to NLP Tasks for Bulgarian: The Verb Paradigm Patterns
Authors: Ivaylo Radev
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation

This paper discusses some possible usages of one unexplored lexical language resource containing Bulgarian verb paradigms and their English translations. This type of data can be used for machine translation, generation of pseudo corpora/language exercises, and evaluation of parsers. Upon completion, the resource will be linked with other existing resources such as the morphological lexicon, valency lexicon, as well as BTB-WordNet.
Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features
Authors: Kim Cheng SHEANG
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Complex Word Identification, Deep Learning, Word Embeddings

The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features.
Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension
Authors: Denis Smirnov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Reading Comprehension, Reading Comprehension

State-of-the-art machine reading comprehension models are capable of producing answers for factual questions about a given piece of text. However, some type of questions requires commonsense knowledge which cannot be inferred from the given text passage. Thus, external semantic information could enhance the performance of these models. This PhD research proposal provides a brief overview of some existing machine reading comprehension datasets and models and outlines possible ways of their improvement.
mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs
Authors: Arindam Sengupta, Feng Jin, Renyuan Zhang, Siyang Cao
Date: 2019-11-21 00:00:00
Tasks: Autonomous Vehicles, Decision Making

In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals. The proposed method would find several applications in traffic monitoring systems, autonomous vehicles, patient monitoring systems and defense forces to detect and track human skeleton for effective and preventive decision making in real-time. The use of radar makes the system operationally robust to scene lighting and adverse weather conditions. The reflected radar point cloud in range, azimuth and elevation are first resolved and projected in Range-Azimuth and Range-Elevation planes. A novel low-size high-resolution radar-to-image representation is also presented, that overcomes the sparsity in traditional point cloud data and offers significant reduction in the subsequent machine learning architecture. The RGB channels were assigned with the normalized values of range, elevation/azimuth and the power level of the reflection signals for each of the points. A forked CNN architecture was used to predict the real-world position of the skeletal joints in 3-D space, using the radar-to-image representation. The proposed method was tested for a single human scenario for four primary motions, (i) Walking, (ii) Swinging left arm, (iii) Swinging right arm, and (iv) Swinging both arms to validate accurate predictions for motion in range, azimuth and elevation. The detailed methodology, implementation, challenges, and validation results are presented.