pwc-archive/papers-with-abstracts huggingface dataset:
Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media
Authors: Hansi Hettiarachchi, Tharindu Ranasinghe
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings combined with emoji embeddings. The approach was evaluated on all three subtasks in Task 6 - SemEval 2019: OffensEval: Identifying and Categorizing Offensive Language in Social Media. The evaluation also showed that even though the capsule networks have not been used commonly in natural language processing tasks, they can outperform existing state of the art solutions for offensive language detection in social media.
EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
Authors: Rayehe Hosseini Pour, Mehrnoush Shamsfard
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: General Classification, Knowledge Graphs, Natural Language Inference, Question Answering, Relation, Relation Classification, Relation Extraction

Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.
Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects
Authors: Nguyen Huy Tien, Le Tung Thanh, Nguyen Minh Le
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Domain Adaptation, Extractive Summarization

Recently research in opinions summarization focuses on rating expressions by aspects and/or sentiments they carry. To extract aspects of an expression, most studies require a predefined list of aspects or at least the number of aspects. Instead of extracting aspects, we rate expressions by aspect similarity recognition (ASR), which evaluates whether two expressions share at least one aspect. This subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. For the ASR subtask, we propose an attention-cell LSTM model, which integrates attention signals into the LSTM gates. According to the experimental results, the attention-cell LSTM works efficiently for learning latent aspects between two sentences in both settings of in-domain and cross-domain. In addition, the proposed extractive summarization method using ASR shows significant improvements over baselines on the Opinosis corpus.
Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization
Authors: Tatsuya Ishigaki, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Document Summarization, Sentence

Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary. However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document. To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document. Our discourse-aware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees. Experimental results showed that our model achieved competitive or better performances against state-of-the-art models in terms of ROUGE scores on the DailyMail dataset. We further conducted manual evaluations. The results showed that our approach also gained the coherence of the output summaries.
Semi-Supervised Induction of POS-Tag Lexicons with Tree Models
Authors: Maciej Janicki
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: POS, POS Tagging, TAG

We approach the problem of POS tagging of morphologically rich languages in a setting where only a small amount of labeled training data is available. We show that a bigram HMM tagger benefits from re-training on a larger untagged text using Baum-Welch estimation. Most importantly, this estimation can be significantly improved by pre-guessing tags for OOV words based on morphological criteria. We consider two models for this task: a character-based recurrent neural network, which guesses the tag from the string form of the word, and a recently proposed graph-based model of morphological transformations. In the latter, the unknown POS tags can be modeled as latent variables in a way very similar to Hidden Markov Tree models and an analogue of the Forward-Backward algorithm can be formulated, which enables us to compute expected values over unknown taggings. We evaluate both the quality of the induced tag lexicon and its impact on the HMM{'}s tagging accuracy. In both tasks, the graph-based morphology model performs significantly better than the RNN predictor. This confirms the intuition that morphologically related words provide useful information about an unknown word{'}s POS tag.
Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks
Authors: Arkadiusz Janz, Maciej Piasecki
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Word Sense Disambiguation

Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses. Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios. In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks. We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used. The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Sk{\l}adnica, KPWr) datasets.
Classification of Micro-Texts Using Sub-Word Embeddings
Authors: Mihir Joshi, Nur Zincir-Heywood
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Authorship Attribution, Classification, General Classification, Word Embeddings

Extracting features and writing styles from short text messages is always a challenge. Short messages, like tweets, do not have enough data to perform statistical authorship attribution. Besides, the vocabulary used in these texts is sometimes improvised or misspelled. Therefore, in this paper, we propose combining four feature extraction techniques namely character n-grams, word n-grams, Flexible Patterns and a new sub-word embedding using the skip-gram model. Our system uses a Multi-Layer Perceptron to utilize these features from tweets to analyze short text messages. This proposed system achieves 85{\%} accuracy, which is a considerable improvement over previous systems.
Using Syntax to Resolve NPE in English
Authors: Payal Khullar, Allen Antony, Manish Shrivastava
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper describes a novel, syntax-based system for automatic detection and resolution of Noun Phrase Ellipsis (NPE) in English. The system takes in free input English text, detects the site of nominal elision, and if present, selects potential antecedent candidates. The rules are built using the syntactic information on ellipsis and its antecedent discussed in previous theoretical linguistics literature on NPE. Additionally, we prepare a curated dataset of 337 sentences from well-known, reliable sources, containing positive and negative samples of NPE. We split this dataset into two parts, and use one part to refine our rules and the other to test the performance of our final system. We get an F1-score of 76.47{\%} for detection and 70.27{\%} for NPE resolution on the testset. To the best of our knowledge, ours is the first system that detects and resolves NPE in English. The curated dataset used for this task, albeit small, covers a wide variety of NPE cases and will be made public for future work.
Is Similarity Visually Grounded? Computational Model of Similarity for the Estonian language
Authors: Claudia Kittask, Eduard Barbu
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Researchers in Computational Linguistics build models of similarity and test them against human judgments. Although there are many empirical studies of the computational models of similarity for the English language, the similarity for other languages is less explored. In this study we are chiefly interested in two aspects. In the first place we want to know how much of the human similarity is grounded in the visual perception. To answer this question two neural computer vision models are used and their correlation with the human derived similarity scores is computed. In the second place we investigate if language influences the similarity computation. To this purpose diverse computational models trained on Estonian resources are evaluated against human judgments
Language-Agnostic Twitter-Bot Detection
Authors: J{\"u}rgen Knauth
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: BIG-bench Machine Learning, Twitter Bot Detection

In this paper we address the problem of detecting Twitter bots. We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots. We use this data to train machine learning classifiers that distinguish between real and bot accounts. We identify features that are easy to extract while still providing good results. We analyze different feature groups based on account specific, tweet specific and behavioral specific features and measure their performance compared to other state of the art bot detection methods. For easy future portability of our work we focus on language-agnostic features. With AdaBoost, the best performing classifier, we achieve an accuracy of 0.988 and an AUC of 0.995. As the creation of good training data in machine learning is often difficult - especially in the domain of Twitter bot detection - we additionally analyze to what extent smaller amounts of training data lead to useful results by reviewing cross-validated learning curves. Our results indicate that using few but expressive features already has a good practical benefit for bot detection, especially if only a small amount of training data is available.
Multi-level analysis and recognition of the text sentiment on the example of consumer opinions
Authors: Jan Koco{\'n}, Monika Za{\'s}ko-Zieli{\'n}ska, Piotr Mi{\l}kowski
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: General Classification, regression

In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).
A Qualitative Evaluation Framework for Paraphrase Identification
Authors: Venelin Kovatchev, M. Antonia Marti, Maria Salamo, Javier Beltran
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Paraphrase Identification

In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems. Our evaluation framework makes use of a PI corpus annotated with linguistic phenomena to provide a better understanding and interpretation of the performance of various PI systems. Our approach allows for a qualitative evaluation and comparison of the PI models using human interpretable categories. It does not require modification of the training objective of the systems and does not place additional burden on the developers. We replicate several popular supervised and unsupervised PI systems. Using our evaluation framework we show that: 1) Each system performs differently with respect to a set of linguistic phenomena and makes qualitatively different kinds of errors; 2) Some linguistic phenomena are more challenging than others across all systems.
Study on Unsupervised Statistical Machine Translation for Backtranslation
Authors: Anush Kumar, Nihal V. Nayak, Ch, Aditya ra, Mydhili K. Nair
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

Machine Translation systems have drastically improved over the years for several language pairs. Monolingual data is often used to generate synthetic sentences to augment the training data which has shown to improve the performance of machine translation models. In our paper, we make use of an Unsupervised Statistical Machine Translation (USMT) to generate synthetic sentences. Our study compares the performance improvements in Neural Machine Translation model when using synthetic sentences from supervised and unsupervised Machine Translation models. Our approach of using USMT for backtranslation shows promise in low resource conditions and achieves an improvement of 3.2 BLEU score over the Neural Machine Translation model.
Towards Functionally Similar Corpus Resources for Translation
Authors: Maria Kunilovskaya, Serge Sharoff
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Translation

The paper describes a computational approach to produce functionally comparable monolingual corpus resources for translation studies and contrastive analysis. We exploit a text-external approach, based on a set of Functional Text Dimensions to model text functions, so that each text can be represented as a vector in a multidimensional space of text functions. These vectors can be used to find reasonably homogeneous subsets of functionally similar texts across different corpora. Our models for predicting text functions are based on recurrent neural networks and traditional feature-based machine learning approaches. In addition to using the categories of the British National Corpus as our test case, we investigated the functional comparability of the English parts from the two parallel corpora: CroCo (English-German) and RusLTC (English-Russian) and applied our models to define functionally similar clusters in them. Our results show that the Functional Text Dimensions provide a useful description for text categories, while allowing a more flexible representation for texts with hybrid functions.
Question Similarity in Community Question Answering: A Systematic Exploration of Preprocessing Methods and Models
Authors: Florian Kunneman, Thiago castro Ferreira, Emiel Krahmer, Antal Van den Bosch
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Community Question Answering, Question Answering, Question Similarity, Translation, Word Translation

Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before. To address this issue, NLP researchers have developed methods to automatically detect question-similarity, which was one of the shared tasks in SemEval. The best performing systems for this task made use of Syntactic Tree Kernels or the SoftCosine metric. However, it remains unclear why these methods seem to work, whether their performance can be improved by better preprocessing methods and what kinds of errors they (and other methods) make. In this paper, we therefore systematically combine and compare these two approaches with the more traditional BM25 and translation-based models. Moreover, we analyze the impact of preprocessing steps (lowercasing, suppression of punctuation and stop words removal) and word meaning similarity based on different distributions (word translation probability, Word2Vec, fastText and ELMo) on the performance of the task. We conduct an error analysis to gain insight into the differences in performance between the system set-ups. The implementation is made publicly available from https://github.com/fkunneman/DiscoSumo/tree/master/ranlp.
A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information
Authors: Sofie Labat, Els Lefever
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Binary Classification, Form, General Classification, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper presents proof-of-concept experiments for combining orthographic and semantic information to distinguish cognates from non-cognates. To this end, a context-independent gold standard is developed by manually labelling English-Dutch pairs of cognates and false friends in bilingual term lists. These annotated cognate pairs are then used to train and evaluate a supervised binary classification system for the automatic detection of cognates. Two types of information sources are incorporated in the classifier: fifteen string similarity metrics capture form similarity between source and target words, while word embeddings model semantic similarity between the words. The experimental results show that even though the system already achieves good results by only incorporating orthographic information, the performance further improves by including semantic information in the form of embeddings.
Resolving Pronouns for a Resource-Poor Language, Malayalam Using Resource-Rich Language, Tamil.
Authors: Sobha Lalitha Devi
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Language Modeling, Language Modelling

In this paper we give in detail how a resource rich language can be used for resolving pronouns for a less resource language. The source language, which is resource rich language in this study, is Tamil and the resource poor language is Malayalam, both belonging to the same language family, Dravidian. The Pronominal resolution developed for Tamil uses CRFs. Our approach is to leverage the Tamil language model to test Malayalam data and the processing required for Malayalam data is detailed. The similarity at the syntactic level between the languages is exploited in identifying the features for developing the Tamil language model. The word form or the lexical item is not considered as a feature for training the CRFs. Evaluation on Malayalam Wikipedia data shows that our approach is correct and the results, though not as good as Tamil, but comparable.
Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates
Authors: Daniil Larionov, Artem Shelmanov, Elena Chistova, Ivan Smirnov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification, Semantic Role Labeling

We build the first full pipeline for semantic role labelling of Russian texts. The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming. We train supervised neural network models for argument classification using Russian semantically annotated corpus {--} FrameBank. However, we note that this resource provides annotations only to a very limited set of predicates. We combat the problem of annotation scarcity by introducing two models that rely on different sets of features: one for {``}known{''} predicates that are present in the training set and one for {``}unknown{''} predicates that are not. We show that the model for {``}unknown{''} predicates can alleviate the lack of annotation by using pretrained embeddings. We perform experiments with various types of embeddings including the ones generated by deep pretrained language models: word2vec, FastText, ELMo, BERT, and show that embeddings generated by deep pretrained language models are superior to classical shallow embeddings for argument classification of both {``}known{''} and {``}unknown{''} predicates.
Structural Approach to Enhancing WordNet with Conceptual Frame Semantics
Authors: Svetlozara Leseva, Ivelina Stoyanova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Relation

This paper outlines procedures for enhancing WordNet with conceptual information from FrameNet. The mapping of the two resources is non-trivial. We define a number of techniques for the validation of the consistency of the mapping and the extension of its coverage which make use of the structure of both resources and the systematic relations between synsets in WordNet and between frames in FrameNet, as well as between synsets and frames). We present a case study on causativity, a relation which provides enhancement complementary to the one using hierarchical relations, by means of linking in a systematic way large parts of the lexicon. We show how consistency checks and denser relations may be implemented on the basis of this relation. We, then, propose new frames based on causative-inchoative correspondences and in conclusion touch on the possibilities for defining new frames based on the types of specialisation that takes place from parent to child synset.
Compositional Hyponymy with Positive Operators
Authors: Martha Lewis
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentence

Language is used to describe concepts, and many of these concepts are hierarchical. Moreover, this hierarchy should be compatible with forming phrases and sentences. We use linear-algebraic methods that allow us to encode words as collections of vectors. The representations we use have an ordering, related to subspace inclusion, which we interpret as modelling hierarchical information. The word representations built can be understood within a compositional distributional semantic framework, providing methods for composing words to form phrase and sentence level representations. We show that the resulting representations give competitive results on both word-level hyponymy and sentence-level entailment datasets.