pwc-archive/papers-with-abstracts huggingface dataset:
Bootstrapping a Romanian Corpus for Medical Named Entity Recognition
Authors: Maria Mitrofan
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Medical Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Relation Extraction

Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, it was created a sub-corpus specific to this domain. In this paper we present a newly developed Romanian sub-corpus for medical-domain NER, which is a valuable asset for the field of biomedical text processing. We provide a description of the sub-corpus, informative statistics about data-composition and we evaluate an automatic NER tool on the newly created resource.
A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information
Authors: Isabel Moreno, Mar{\'\i}a Teresa Rom{\'a}-Ferri, Paloma Moreda Pozo
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification, Named Entity Recognition (NER), Question Answering, Text Generation, Text Summarization

This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.
Similarity Based Genre Identification for POS Tagging Experts \& Dependency Parsing
Authors: Atreyee Mukherjee, S K{\"u}bler, ra
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Dependency Parsing, Domain Adaptation, POS, POS Tagging, Sentence

POS tagging and dependency parsing achieve good results for homogeneous datasets. However, these tasks are much more difficult on heterogeneous datasets. In (Mukherjee et al. 2016, 2017), we address this issue by creating genre experts for both POS tagging and parsing. We use topic modeling to automatically separate training and test data into genres and to create annotation experts per genre by training separate models for each topic. However, this approach assumes that topic modeling is performed jointly on training and test sentences each time a new test sentence is encountered. We extend this work by assigning new test sentences to their genre expert by using similarity metrics. We investigate three different types of methods: 1) based on words highly associated with a genre by the topic modeler, 2) using a k-nearest neighbor classification approach, and 3) using perplexity to determine the closest topic. The results show that the choice of similarity metric has an effect on results and that we can reach comparable accuracies to the joint topic modeling in POS tagging and dependency parsing, thus providing a viable and efficient approach to POS tagging and parsing a sentence by its genre expert.
Recognizing Reputation Defence Strategies in Critical Political Exchanges
Authors: Nona Naderi, Graeme Hirst
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification, Relation, Relation Classification

We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and reputation defence, we determine the reputation defence strategy. We annotate a dataset of parliamentary questions and answers with reputation defence strategies. We then propose a model based on supervised learning to address the detection of these strategies, and report promising experimental results.
Classifying Frames at the Sentence Level in News Articles
Authors: Nona Naderi, Graeme Hirst
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, General Classification, Semantic Textual Similarity, Sentence, Time Series Analysis, Topic Models

Previous approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames at the sentence level in news articles. We conduct our experiments on the publicly available Media Frames Corpus compiled from the U.S. Newspapers. Using (B)LSTMs and GRU networks to represent the meaning of frames, we demonstrate that our approach yields at least 14-point improvement over several baseline methods.
Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums
Authors: Preslav Nakov, Tsvetomila Mihaylova, Llu{\'\i}s M{\`a}rquez, Yashkumar Shiroya, Ivan Koychev
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Community Question Answering, Information Retrieval, Question Answering

We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted. First, we motivate the problem and we create a publicly available annotated English corpus by crowdsourcing. Second, we propose a large set of features to predict the credibility of the answers. The features model the user, the answer, the question, the thread as a whole, and the interaction between them. Our experiments with ranking SVMs show that the credibility labels can be predicted with high performance according to several standard IR ranking metrics, thus supporting the potential usage of this layer of credibility information in practical applications. The features modeling the profile of the user (in particular trollness) turn out to be most important, but embedding features modeling the answer and the similarity between the question and the answer are also very relevant. Overall, half of the gap between the baseline performance and the perfect classifier can be covered using the proposed features.
Bulgarian-English and English-Bulgarian Machine Translation: System Design and Evaluation
Authors: Petya Osenova, Kiril Simov
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Translation

The paper presents a deep factored machine translation (MT) system between English and Bulgarian languages in both directions. The MT system is hybrid. It consists of three main steps: (1) the source-language text is linguistically annotated, (2) it is translated to the target language with the Moses system, and (3) translation is post-processed with the help of the transferred linguistic annotation from the source text. Besides automatic evaluation we performed manual evaluation over a domain test suite of sentences demonstrating certain phenomena like imperatives, questions, etc.
Identification of Character Adjectives from Mahabharata
Authors: Apurba Paul, Dipankar Das
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the tra-ditional approaches of named entity identifica-tion, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the pres-ence of characters in different text spans. Such six patterns were used in order to extract the cha-racters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.
Learning Multimodal Gender Profile using Neural Networks
Authors: Carlos P{\'e}rez Estruch, Roberto Paredes Palacios, Paolo Rosso
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Author Profiling

Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8{\%}) obtaining the state-of-the-art performance of 91.3{\%}.
Recognition of Genuine Polish Suicide Notes
Authors: Maciej Piasecki, Ksenia M{\l}ynarczyk, Jan Koco{\'n}
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification

In this article we present the result of the recent research in the recognition of genuine Polish suicide notes (SNs). We provide useful method to distinguish between SNs and other types of discourse, including counterfeited SNs. The method uses a wide range of word-based and semantic features and it was evaluated using Polish Corpus of Suicide Notes, which contains 1244 genuine SNs, expanded with manually prepared set of 334 counterfeited SNs and 2200 letter-like texts from the Internet. We utilized the algorithm to create the class-related sense dictionaries to improve the result of SNs classification. The obtained results show that there are fundamental differences between genuine SNs and counterfeited SNs. The applied method of the sense dictionary construction appeared to be the best way of improving the model.
Cross-Lingual SRL Based upon Universal Dependencies
Authors: Ond{\v{r}}ej Pra{\v{z}}{\'a}k, Miloslav Konop{\'\i}k
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Semantic Parsing, Semantic Role Labeling

In this paper, we introduce a cross-lingual Semantic Role Labeling (SRL) system with language independent features based upon Universal Dependencies. We propose two methods to convert SRL annotations from monolingual dependency trees into universal dependency trees. Our SRL system is based upon cross-lingual features derived from universal dependency trees and a supervised learning that utilizes a maximum entropy classifier. We design experiments to verify whether the Universal Dependencies are suitable for the cross-lingual SRL. The results are very promising and they open new interesting research paths for the future.
Using Gaze Data to Predict Multiword Expressions
Authors: Omid Rohanian, Shiva Taslimipoor, Victoria Yaneva, Le An Ha
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Part-Of-Speech Tagging, POS, Word Embeddings

In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena. In this paper we conduct a preliminary study towards the automatic identification of multiword expressions based on gaze features from native and non-native speakers of English. We report comparisons between a part-of-speech (POS) and frequency baseline to: i) a prediction model based solely on gaze data and ii) a combined model of gaze data, POS and frequency. In spite of the challenging nature of the task, best performance was achieved by the latter. Furthermore, we explore how the type of gaze data (from native versus non-native speakers) affects the prediction, showing that data from the two groups is discriminative to an equal degree for the task. Finally, we show that late processing measures are more predictive than early ones, which is in line with previous research on idioms and other formulaic structures.
Real-Time News Summarization with Adaptation to Media Attention
Authors: Andreas R{\"u}ckl{\'e}, Iryna Gurevych
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, Decision Making, News Summarization

Real-time summarization of news events (RTS) allows persons to stay up-to-date on important topics that develop over time. With the occurrence of major sub-events, media attention increases and a large number of news articles are published. We propose a summarization approach that detects such changes and selects a suitable summarization configuration at run-time. In particular, at times with high media attention, our approach exploits the redundancy in content to produce a more precise summary and avoid emitting redundant information. We find that our approach significantly outperforms a strong non-adaptive RTS baseline in terms of the emitted summary updates and achieves the best results on a recent web-scale dataset. It can successfully be applied to a different real-world dataset without requiring additional modifications.
Measuring the Limit of Semantic Divergence for English Tweets.
Authors: Dwijen Rudrapal, Amitava Das
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Question Answering, Sentence, Text Summarization, Translation

In human language, an expression could be conveyed in many ways by different people. Even that the same person may express same sentence quite differently when addressing different audiences, using different modalities, or using different syntactic variations or may use different set of vocabulary. The possibility of such endless surface form of text while the meaning of the text remains almost same, poses many challenges for Natural Language Processing (NLP) systems like question-answering system, machine translation system and text summarization. This research paper is an endeavor to understand the characteristic of such endless semantic divergence. In this research work we develop a corpus of 1525 semantic divergent sentences for 200 English tweets.
Introducing EVALD -- Software Applications for Automatic Evaluation of Discourse in Czech
Authors: Kate{\v{r}}ina Rysov{\'a}, Magdal{\'e}na Rysov{\'a}, Ji{\v{r}}{\'\i} M{\'\i}rovsk{\'y}, Michal Nov{\'a}k
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

In the paper, we introduce two software applications for automatic evaluation of coherence in Czech texts called EVALD {--} Evaluator of Discourse. The first one {--} EVALD 1.0 {--} evaluates texts written by native speakers of Czech on a five-step scale commonly used at Czech schools (grade 1 is the best, grade 5 is the worst). The second application is EVALD 1.0 for Foreigners assessing texts by non-native speakers of Czech using six-step scale (A1{--}C2) according to CEFR. Both appli-cations are available online at \url{https://lindat.mff.cuni.cz/services/evald-foreign/}.
Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier
Authors: Giancarlo Salton, Robert Ross, John Kelleher
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: BIG-bench Machine Learning, Vocal Bursts Type Prediction

In our work we address limitations in the state-of-the-art in idiom type identification. We investigate different approaches for a lexical fixedness metric, a component of the state-of the-art model. We also show that our Machine Learning based approach to the idiom type identification task achieves an F1-score of 0.85, an improvement of 11 points over the state-of the-art.
A Calibration Method for Evaluation of Sentiment Analysis
Authors: F. Sharmila Satthar, Roger Evans, Gulden Uchyigit
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Information Retrieval, Sentiment Analysis

Sentiment analysis is the computational task of extracting sentiment from a text document {--} for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced in recent years, using a range of different techniques to extract sentiment information from a document. Measuring these methods against a gold standard dataset is a useful way to evaluate such systems. However, different sentiment analysis techniques represent sentiment values in different ways, such as discrete categorical classes or continuous numerical sentiment scores. This creates a challenge for evaluating and comparing such systems; in particular assessing numerical scores against datasets that use fixed classes is difficult, because the numerical outputs have to be mapped onto the ordered classes. This paper proposes a novel calibration technique that uses precision vs. recall curves to set class thresholds to optimize a continuous sentiment analyser{'}s performance against a discrete gold standard dataset. In experiments mapping a continuous score onto a three-class classification of movie reviews, we show that calibration results in a substantial increase in f-score when compared to a non-calibrated mapping.
Building Multiword Expressions Bilingual Lexicons for Domain Adaptation of an Example-Based Machine Translation System
Authors: Nasredine Semmar, Mariama Laib
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Domain Adaptation, Information Retrieval, Machine Translation, Translation

We describe in this paper a hybrid ap-proach to build automatically bilingual lexicons of Multiword Expressions (MWEs) from parallel corpora. We more specifically investigate the impact of using a domain-specific bilingual lexicon of MWEs on domain adaptation of an Example-Based Machine Translation (EBMT) system. We conducted experiments on the English-French language pair and two kinds of texts: in-domain texts from Europarl (European Parliament proceedings) and out-of-domain texts from Emea (European Medicines Agency documents) and Ecb (European Central Bank corpus). The obtained results indicate that integrating domain-specific bilingual lexicons of MWEs improves translation quality of the EBMT system when texts to translate are related to the specific domain and induces a relatively slight deterioration of translation quality when translating general-purpose texts.
Identifying the Authors' National Variety of English in Social Media Texts
Authors: Vasiliki Simaki, Panagiotis Simakis, Carita Paradis, Andreas Kerren
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: feature selection, General Classification, POS

In this paper, we present a study for the identification of authors{'} national variety of English in texts from social media. In data from Facebook and Twitter, information about the author{'}s social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and data-based features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32{\%}. The experimental results are evaluated, and the efficacy of the ranked features discussed.
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
Authors: Kiril Simov, Svetla Boytcheva, Petya Osenova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, Language Modeling, Language Modelling, Learning Word Embeddings, Word Embeddings, Word Sense Disambiguation

Word vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text (e.g. Wikipedia or newswire articles) or artificially-generated pseudo corpora due to natural data sparseness. We exploit Lexical Chain based templates over Knowledge Graph for generating pseudo-corpora with controlled linguistic value. These corpora are then used for learning word embeddings. A number of experiments have been conducted over the following test sets: WordSim353 Similarity, WordSim353 Relatedness and SimLex-999. The results show that, on the one hand, the incorporation of many-relation lexical chains improves results, but on the other hand, unrestricted-length chains remain difficult to handle with respect to their huge quantity.
Word Embeddings as Features for Supervised Coreference Resolution
Authors: Iliana Simova, Hans Uszkoreit
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: coreference-resolution, Coreference Resolution, Dimensionality Reduction, Machine Translation, Question Answering, Relation Extraction, Word Embeddings

A common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have been shown successful in encoding relatedness between words, could potentially be a good source of such knowledge. Moreover, being obtained in an unsupervised manner, they could help address data sparsity issues in labeled training data at a small cost. In this work we investigate whether and to what extend features derived from word embeddings can be successfully used for supervised coreference resolution. We experiment with several word embedding models, and several different types of embeddingbased features, including embedding cluster and cosine similarity-based features. Our evaluations show improvements in the performance of a supervised state-of-theart coreference system.
Cross-lingual Flames Detection in News Discussions
Authors: Josef Steinberger, Tom{\'a}{\v{s}} Brychc{\'\i}n, Tom{\'a}{\v{s}} Hercig, Peter Krejzl
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sarcasm Detection, Sentiment Analysis, Stance Detection

We introduce Flames Detector, an online system for measuring flames, i.e. strong negative feelings or emotions, insults or other verbal offences, in news commentaries across five languages. It is designed to assist journalists, public institutions or discussion moderators to detect news topics which evoke wrangles. We propose a machine learning approach to flames detection and calculate an aggregated score for a set of comment threads. The demo application shows the most flaming topics of the current period in several language variants. The search functionality gives a possibility to measure flames in any topic specified by a query. The evaluation shows that the flame detection in discussions is a difficult task, however, the application can already reveal interesting information about the actual news discussions.
Pyramid-based Summary Evaluation Using Abstract Meaning Representation
Authors: Josef Steinberger, Peter Krejzl, Tom{\'a}{\v{s}} Brychc{\'\i}n
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Abstract Meaning Representation, Graph Similarity, Sentence

We propose a novel metric for evaluating summary content coverage. The evaluation framework follows the Pyramid approach to measure how many summarization content units, considered important by human annotators, are contained in an automatic summary. Our approach automatizes the evaluation process, which does not need any manual intervention on the evaluated summary side. Our approach compares abstract meaning representations of each content unit mention and each summary sentence. We found that the proposed metric complements well the widely-used ROUGE metrics.