pwc-archive/papers-with-abstracts huggingface dataset:
Russian-Tatar Socio-Political Thesaurus: Methodology, Challenges, the Status of the Project
Authors: Alfiya Galieva, Olga Nevzorova, Dilyara Yakubova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Specificity

This paper discusses the general methodology and important practical aspects of implementing a new bilingual lexical resource {--} the Russian-Tatar Socio-Political Thesaurus that is being developed on the basis of the Russian RuThes thesaurus format as a hierarchy of concepts viewed as units of thought. Each concept is linked with a set of language expressions (words and collocations) referring to it in texts (text entries). Currently the Russian-Tatar Socio-Political Thesaurus includes 6,000 concepts, while new concepts and text entries are being constantly added to it. The paper outlines main challenges of translating concept names and their text entries into Tatar, and describes ways of reflecting the specificity of the Tatar lexical-semantic system.
On a Chat Bot Finding Answers with Optimal Rhetoric Representation
Authors: Boris Galitsky, Dmitry Ilvovsky
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sentence, valid

We demo a chat bot with the focus on complex, multi-sentence questions that enforce what we call rhetoric agreement of answers with questions. Chat bot finds answers which are not only relevant by topic but also match the question by style, argumentation patterns, communication means, experience level and other attributes. The system achieves rhetoric agreement by learning pairs of discourse trees (DTs) for question (Q) and answer (A). We build a library of best answer DTs for most types of complex questions. To better recognize a valid rhetoric agreement between Q and A, DTs are extended with the labels for communicative actions. An algorithm for finding the best DT for an A, given a Q, is evaluated.
A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates
Authors: Pepa Gencheva, Preslav Nakov, Llu{\'\i}s M{\`a}rquez, Alberto Barr{\'o}n-Cede{\~n}o, Ivan Koychev
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Fact Checking

In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Hashtag Processing for Enhanced Clustering of Tweets
Authors: Dagmar Gromann, Thierry Declerck
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Clustering, Entity Disambiguation, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval, Text Retrieval

Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.
Natural Language Processing Technologies for Document Profiling
Authors: Antonio Guill{\'e}n, Yoan Guti{\'e}rrez, Rafael Mu{\~n}oz
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles

Nowadays, search for documents on the Internet is becoming increasingly difficult. The reason is the amount of content published by users (articles, comments, blogs, reviews). How to facilitate that the users can find their required documents? What would be necessary to provide useful document meta-data for supporting search engines? In this article, we present a study of some Natural Language Processing (NLP) technologies that can be useful for facilitating the proper identification of documents according to the user needs. For this purpose, it is designed a document profile that will be able to represent semantic meta-data extracted from documents by using NLP technologies. The research is basically focused on the study of different NLP technologies in order to support the creation our novel document profile proposal from semantic perspectives.
MappSent: a Textual Mapping Approach for Question-to-Question Similarity
Authors: Amir Hazem, Basma El Amel Boussaha, Hern, Nicolas ez
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Community Question Answering, Question Answering, Question Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Word Embeddings

Since the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. Mikolov et al. (2013) have demonstrated that words and phrases exhibit linear structures that allow to meaningfully combine words by an element-wise addition of their vector representations. Recently, Arora et al. (2017) have shown that removing the projections of the weighted average sum of word embedding vectors on their first principal components, outperforms sophisticated supervised methods including RNN{'}s and LSTM{'}s. Inspired by Mikolov et al. (2013) and Arora et al. (2017) findings and by a bilingual word mapping technique presented in Artetxe et al. (2016), we introduce MappSent, a novel approach for textual similarity. Based on a linear sentence embedding representation, its principle is to build a matrix that maps sentences in a joint-subspace where similar sets of sentences are pushed closer. We evaluate our approach on the SemEval 2016/2017 question-to-question similarity task and show that overall MappSent achieves competitive results and outperforms in most cases state-of-art methods.
The Impact of Figurative Language on Sentiment Analysis
Authors: Tom{\'a}{\v{s}} Hercig, Ladislav Lenc
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sarcasm Detection, Sentiment Analysis

Figurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits of sentiment analysis of supposedly literal texts. We explore the effect of figurative language on sentiment analysis. We incorporate the figurative language indicators into the sentiment analysis process and compare the results with and without the additional information about them. We evaluate on the SemEval-2015 Task 11 data and outperform the first team with our convolutional neural network model and additional training data in terms of mean squared error and we follow closely behind the first place in terms of cosine similarity.
Non-Deterministic Segmentation for Chinese Lattice Parsing
Authors: Hai Hu, Daniel Dakota, S K{\"u}bler, ra
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Morphological Analysis, Segmentation, Sentence

Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.
Good News vs. Bad News: What are they talking about?
Authors: Olga Kanishcheva, Victoria Bobicev
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sentiment Analysis, text annotation

Today{'}s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people{'}s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.
We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!
Authors: Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Author Profiling, Clickbait Detection, Word Embeddings

It is completely amazing! Fake news and {``}click baits{''} have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason {\#}2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research, and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and clickbait in the Bulgarian cyberspace. You won{'}t believe what we have found!
Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition
Authors: Divyansh Kaushik, Shashank Gupta, Chakradhar Raju, Reuben Dias, Sanjib Ghosh
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and e-tickets are usually machine generated by filling slots in pre-defined templates which improve the presentation of such emails but also make the emails more complex in structure. Extracting the relevant information from these emails would let users track their journeys and important updates on applications installed on their devices to give them a consolidated over view of their itineraries and also save valuable time. We investigate the use of an HMM-based named entity recognizer on such emails which we will use to label and extract relevant entities. NER in such emails is challenging as these itineraries offer less useful contextual information. We also propose a rich set of features which are integrated into the model and are specific to our domain. The result from our model is a list of lists containing the relevant information extracted from ones itinerary.
Graph-Based Approach to Recognizing CST Relations in Polish Texts
Authors: Pawe{\l} K{\k{e}}dzia, Maciej Piasecki, Arkadiusz Janz
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Document Summarization, Graph Similarity, Information Retrieval, Multi-Document Summarization, Natural Language Inference, text similarity

This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.
Improved Recognition and Normalisation of Polish Temporal Expressions
Authors: Jan Koco{\'n}, Micha{\l} Marci{\'n}czuk
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: BIG-bench Machine Learning, Question Answering

In this article we present the result of the recent research in the recognition and normalisation of Polish temporal expressions. The temporal information extracted from the text plays major role in many information extraction systems, like question answering, event recognition or discourse analysis. We proposed a new method for the temporal expressions normalisation, called Cascade of Partial Rules. Here we describe results achieved by updated version of Liner2 machine learning system.
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees
Authors: Michal Konkol
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Named Entity Recognition (NER), Question Answering, Sentence, Sentiment Analysis, Word Embeddings, Word Similarity

In this paper, we introduce WoRel, a model that jointly learns word embeddings and a semantic representation of word relations. The model learns from plain text sentences and their dependency parse trees. The word embeddings produced by WoRel outperform Skip-Gram and GloVe in word similarity and syntactical word analogy tasks and have comparable results on word relatedness and semantic word analogy tasks. We show that the semantic representation of relations enables us to express the meaning of phrases and is a promising research direction for semantics at the sentence level.
Czech Dataset for Semantic Similarity and Relatedness
Authors: Miloslav Konop{\'\i}k, Ond{\v{r}}ej Pra{\v{z}}{\'a}k, David Steinberger
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Semantic Similarity, Semantic Textual Similarity

This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is $r = 0.81$. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.
Extracting semantic relations via the combination of inferences, schemas and cooccurrences
Authors: Mathieu Lafourcade, Nathalie Le Brun
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

Extracting semantic relations from texts is a good way to build and supply a knowledge base, an indispensable resource for text analysis. We propose and evaluate the combination of three ways of producing lexical-semantic relations.
If mice were reptiles, then reptiles could be mammals or How to detect errors in the JeuxDeMots lexical network?
Authors: Mathieu Lafourcade, Alain Joubert, Nathalie Le Brun
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

Correcting errors in a data set is a critical issue. This task can be either hand-made by experts, or by crowdsourcing methods, or automatically done using algorithms. Although the rate of errors present in the JeuxDeMots network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect {``}erroneous{''} initial relations, which could lead to the automatic detection of the majority of errors in the network.
Word Embeddings for Multi-label Document Classification
Authors: Ladislav Lenc, Pavel Kr{\'a}l
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Classification, Document Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentiment Analysis, Text Classification, Word Embeddings

In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech {\v{C}}TK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.
Gender Prediction for Chinese Social Media Data
Authors: Wen Li, Markus Dickinson
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Chinese Word Segmentation, Gender Prediction, Prediction, Sentiment Analysis, Word Embeddings

Social media provides users a platform to publish messages and socialize with others, and microblogs have gained more users than ever in recent years. With such usage, user profiling is a popular task in computational linguistics and text mining. Different approaches have been used to predict users{'} gender, age, and other information, but most of this work has been done on English and other Western languages. The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform. Given issues in Chinese word segmentation, we explore character and word n-grams as features for this task, as well as using character and word embeddings for classification. Given how the data is extracted, we approach the task on a per-post basis, and we show the difficulties of the task for both humans and computers. Nonetheless, we present encouraging results and point to future improvements.
A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing
Authors: Zhihua Liao, Yan Xie
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Semantic Parsing, Sentence, Translation

In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.
Summarizing World Speak : A Preliminary Graph Based Approach
Authors: Nikhil Londhe, Rohini Srihari
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Document Summarization, Multi-Document Summarization

Social media platforms play a crucial role in piecing together global news stories via their corresponding online discussions. Thus, in this work, we introduce the problem of automatically summarizing massively multilingual microblog text streams. We discuss the challenges involved in both generating summaries as well as evaluating them. We introduce a simple word graph based approach that utilizes node neighborhoods to identify keyphrases and thus in turn, pick summary candidates. We also demonstrate the effectiveness of our method in generating precise summaries as compared to other popular techniques.
Inforex --- a collaborative system for text corpora annotation and analysis
Authors: Micha{\l} Marci{\'n}czuk, Marcin Oleksy, Jan Koco{\'n}
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Named Entity Recognition (NER), Word Sense Disambiguation

We report a first major upgrade of Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. Inforex is a part of Polish CLARIN infrastructure. It is integrated with a digital repository for storing and publishing language resources and allows to visualize, browse and annotate text corpora stored in the repository. As a result of a series of workshops for researches from humanities and social sciences fields we improved the graphical interface to make the system more friendly and readable for non-experienced users. We also implemented a new functionality for gold standard annotation which includes private annotations and annotation agreement by a super-annotator.
Lemmatization of Multi-word Common Noun Phrases and Named Entities in Polish
Authors: Micha{\l} Marci{\'n}czuk
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Lemmatization

In the paper we present a tool for lemmatization of multi-word common noun phrases and named entities for Polish called LemmaPL. The tool is based on a set of manually crafted rules and heuristics utilizing a set of dictionaries (including morphological, named entities and inflection patterns). The accuracy of lemmatization obtained by the tool reached 97.99{\%} on a dataset with multi-word common noun phrases and 86.17{\%} for case-sensitive evaluation on a dataset with named entities.
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
Authors: Chenggang Mi, Yating Yang, Rui Dong, Xi Zhou, Lei Wang, Xiao Li, Tonghai Jiang
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Segmentation, Translation, Word Alignment

To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.