pwc-archive/papers-with-abstracts huggingface dataset:
A Classification-based Approach to Economic Event Detection in Dutch News Text
Authors: Els Lefever, V{\'e}ronique Hoste
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Event Detection, General Classification

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.
Predictive Modeling: Guessing the NLP Terms of Tomorrow
Authors: Gil Francopoulo, Joseph Mariani, Patrick Paroubek
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Predictive modeling, often called {``}predictive analytics{''} in a commercial context, encompasses a variety of statistical techniques that analyze historical and present facts to make predictions about unknown events. Often the unknown events are in the future, but prediction can be applied to any type of unknown whether it be in the past or future. In our case, we present some experiments applying predictive modeling to the usage of technical terms within the NLP domain.
The Gavagai Living Lexicon
Authors: Magnus Sahlgren, Amaru Cuba Gyllensten, Fredrik Espinoza, Ola Hamfors, Jussi Karlgren, Fredrik Olsson, Per Persson, Akshay Viswanathan, Anders Holst
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents the Gavagai Living Lexicon, which is an online distributional semantic model currently available in 20 different languages. We describe the underlying distributional semantic model, and how we have solved some of the challenges in applying such a model to large amounts of streaming data. We also describe the architecture of our implementation, and discuss how we deal with continuous quality assurance of the lexicon.
Arabic to English Person Name Transliteration using Twitter
Authors: Hamdy Mubarak, Ahmed Abdelali
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Retrieval, Translation, Transliteration

Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data.
Korean TimeML and Korean TimeBank
Authors: Young-Seob Jeong, Won-Tae Joo, Hyun-Woo Do, Chae-Gyun Lim, Key-Sun Choi, Ho-Jin Choi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Many emerging documents usually contain temporal information. Because the temporal information is useful for various applications, it became important to develop a system of extracting the temporal information from the documents. Before developing the system, it first necessary to define or design the structure of temporal information. In other words, it is necessary to design a language which defines how to annotate the temporal information. There have been some studies about the annotation languages, but most of them was applicable to only a specific target language (e.g., English). Thus, it is necessary to design an individual annotation language for each language. In this paper, we propose a revised version of Koreain Time Mark-up Language (K-TimeML), and also introduce a dataset, named Korean TimeBank, that is constructed basd on the K-TimeML. We believe that the new K-TimeML and Korean TimeBank will be used in many further researches about extraction of temporal information.
A Large DataBase of Hypernymy Relations Extracted from the Web.
Authors: Julian Seitner, Christian Bizer, Kai Eckert, Stefano Faralli, Robert Meusel, Heiko Paulheim, Simone Paolo Ponzetto
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Word Sense Disambiguation

Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store them effectively into a large database. This collection of relations represents a rich source of knowledge and may be useful for many researchers. We offer the tuple dataset for public download and an Application Programming Interface (API) to help other researchers programmatically query the database.
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems
Authors: Nikolaos Katris, Richard Sutcliffe, Theodore Kalamboukis
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Language Modeling, Language Modelling, Machine Translation, Retrieval, Translation

The objective of this paper was to evaluate the performance of two statistical machine translation (SMT) systems within a cross-language information retrieval (CLIR) architecture and examine if there is a correlation between translation quality and CLIR performance. The SMT systems were KantanMT, a cloud-based machine translation (MT) platform, and Moses, an open-source MT application. First we trained both systems using the same language resources: the EMEA corpus for the translation model and language model and the QTLP corpus for tuning. Then we translated the 63 queries of the OHSUMED test collection from Greek into English using both MT systems. Next, we ran the queries on the document collection using Apache Solr to get a list of the top ten matches. The results were compared to the OHSUMED gold standard. KantanMT achieved higher average precision and F-measure than Moses, while both systems produced the same recall score. We also calculated the BLEU score for each system using the ECDC corpus. Moses achieved a higher BLEU score than KantanMT. Finally, we also tested the IR performance of the original English queries. This work overall showed that CLIR performance can be better even when BLEU score is worse.
Two Decades of Terminology: European Framework Programmes Titles
Authors: Gabriella Pardelli, Sara Goggi, Silvia Giannini, Stefania Biagioni
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Vocal Bursts Valence Prediction

This work analyses a corpus made of the titles of research projects belonging to the last four European Commission Framework Programmes (FP4, FP5, FP6, FP7) during a time span of nearly two decades (1994-2012). The starting point is the idea of creating a corpus of titles which would constitute a terminological niche, a sort of {``}cluster map{''} offering an overall vision on the terms used and the links between them. Moreover, by performing a terminological comparison over a period of time it is possible to trace the presence of obsolete words in outdated research areas as well as of neologisms in the most recent fields. Within this scenario, the minimal purpose is to build a corpus of titles of European projects belonging to the several Framework Programmes in order to obtain a terminological mapping of relevant words in the various research areas: particularly significant would be those terms spread across different domains or those extremely tied to a specific domain. A term could actually be found in many fields and being able to acknowledge and retrieve this cross-presence means being able to linking those different domains by means of a process of terminological mapping.
Legal Text Interpretation: Identifying Hohfeldian Relations from Text
Authors: Wim Peters, Adam Wyner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations of Duty, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and the bearers of associated roles. The outcome of the analysis supports scholars in identifying Hohfeldian structures in legal text when performing close reading of the texts. A cyclic workflow involving automated annotation and expert feedback will incrementally increase the quality and coverage of the automatic extraction process, and increasingly reduce the amount of manual work required of the scholar.
Analysis of English Spelling Errors in a Word-Typing Game
Authors: Ryuichi Tachibana, Mamoru Komachi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The emergence of the web has necessitated the need to detect and correct noisy consumer-generated texts. Most of the previous studies on English spelling-error extraction collected English spelling errors from web services such as Twitter by using the edit distance or from input logs utilizing crowdsourcing. However, in the former approach, it is not clear which word corresponds to the spelling error, and the latter approach requires an annotation cost for the crowdsourcing. One notable exception is Rodrigues and Rytting (2012), who proposed to extract English spelling errors by using a word-typing game. Their approach saves the cost of crowdsourcing, and guarantees an exact alignment between the word and the spelling error. However, they did not assert whether the extracted spelling error corpora reflect the usual writing process such as writing a document. Therefore, we propose a new correctable word-typing game that is more similar to the actual writing process. Experimental results showed that we can regard typing-game logs as a source of spelling errors.
Finding Definitions in Large Corpora with Sketch Engine
Authors: Vojt{\v{e}}ch Kov{\'a}{\v{r}}, Monika Mo{\v{c}}iarikov{\'a}, Pavel Rychl{\'y}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

The paper describes automatic definition finding implemented within the leading corpus query and management tool, Sketch Engine. The implementation exploits complex pattern-matching queries in the corpus query language (CQL) and the indexing mechanism of word sketches for finding and storing definition candidates throughout the corpus. The approach is evaluated for Czech and English corpora, showing that the results are usable in practice: precision of the tool ranges between 30 and 75 percent (depending on the major corpus text types) and we were able to extract nearly 2 million definition candidates from an English corpus with 1.4 billion words. The feature is embedded into the interface as a concordance filter, so that users can search for definitions of any query to the corpus, including very specific multi-word queries. The results also indicate that ordinary texts (unlike explanatory texts) contain rather low number of definitions, which is perhaps the most important problem with automatic definition finding in general.
Improving Information Extraction from Wikipedia Texts using Basic English
Authors: Teresa Rodr{\'\i}guez-Ferreira, Adri{\'a}n Rabad{\'a}n, Raquel Herv{\'a}s, Alberto D{\'\i}az
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The aim of this paper is to study the effect that the use of Basic English versus common English has on information extraction from online resources. The amount of online information available to the public grows exponentially, and is potentially an excellent resource for information extraction. The problem is that this information often comes in an unstructured format, such as plain text. In order to retrieve knowledge from this type of text, it must first be analysed to find the relevant details, and the nature of the language used can greatly impact the quality of the extracted information. In this paper, we compare triplets that represent definitions or properties of concepts obtained from three online collaborative resources (English Wikipedia, Simple English Wikipedia and Simple English Wiktionary) and study the differences in the results when Basic English is used instead of common English. The results show that resources written in Basic English produce less quantity of triplets, but with higher quality.
NLP and Public Engagement: The Case of the Italian School Reform
Authors: Tommaso Caselli, Giovanni Moretti, Rachele Sprugnoli, Sara Tonelli, Damien Lanfrey, Donatella Solda Kutzmann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper we present PIERINO (PIattaforma per l{'}Estrazione e il Recupero di INformazione Online), a system that was implemented in collaboration with the Italian Ministry of Education, University and Research to analyse the citizens{'} comments given in {\#}labuonascuola survey. The platform includes various levels of automatic analysis such as key-concept extraction and word co-occurrences. Each analysis is displayed through an intuitive view using different types of visualizations, for example radar charts and sunburst. PIERINO was effectively used to support shaping the last Italian school reform, proving the potential of NLP in the context of policy making.
Evaluating Translation Quality and CLIR Performance of Query Sessions
Authors: Xabier Saralegi, Eneko Agirre, I{\~n}aki Alegria
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Retrieval, Translation

This paper presents the evaluation of the translation quality and Cross-Lingual Information Retrieval (CLIR) performance when using session information as the context of queries. The hypothesis is that previous queries provide context that helps to solve ambiguous translations in the current query. We tested several strategies on the TREC 2010 Session track dataset, which includes query reformulations grouped by generalization, specification, and drifting types. We study the Basque to English direction, evaluating both the translation quality and CLIR performance, with positive results in both cases. The results show that the quality of translation improved, reducing error rate by 12{\%} (HTER) when using session information, which improved CLIR results 5{\%} (nDCG). We also provide an analysis of the improvements across the three kinds of sessions: generalization, specification, and drifting. Translation quality improved in all three types (generalization, specification, and drifting), and CLIR improved for generalization and specification sessions, preserving the performance in drifting sessions.
Construction and Analysis of a Large Vietnamese Text Corpus
Authors: Dieu-Thu Le, Uwe Quasthoff
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Sentence

This paper presents a new Vietnamese text corpus which contains around 4.05 billion words. It is a collection of Wikipedia texts, newspaper articles and random web texts. The paper describes the process of collecting, cleaning and creating the corpus. Processing Vietnamese texts faced several challenges, for example, different from many Latin languages, Vietnamese language does not use blanks for separating words, hence using common tokenizers such as replacing blanks with word boundary does not work. A short review about different approaches of Vietnamese tokenization is presented together with how the corpus has been processed and created. After that, some statistical analysis on this data is reported including the number of syllable, average word length, sentence length and topic analysis. The corpus is integrated into a framework which allows searching and browsing. Using this web interface, users can find out how many times a particular word appears in the corpus, sample sentences where this word occurs, its left and right neighbors.
Forecasting Emerging Trends from Scientific Literature
Authors: Kartik Asooja, Georgeta Bordea, Gabriela Vulcu, Paul Buitelaar
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: regression, Time Series, Time Series Analysis, Time Series Forecasting

Text analysis methods for the automatic identification of emerging technologies by analyzing the scientific publications, are gaining attention because of their socio-economic impact. The approaches so far have been mainly focused on retrospective analysis by mapping scientific topic evolution over time. We propose regression based approaches to predict future keyword distribution. The prediction is based on historical data of the keywords, which in our case, are LREC conference proceedings. Considering the insufficient number of data points available from LREC proceedings, we do not employ standard time series forecasting methods. We form a dataset by extracting the keywords from previous year proceedings and quantify their yearly relevance using tf-idf scores. This dataset additionally contains ranked lists of related keywords and experts for each keyword.
Extracting Structured Scholarly Information from the Machine Translation Literature
Authors: Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, Daniel Marcu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Reading Comprehension, Translation

Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work. We introduce a structured, queryable representation for experimental results and a baseline system that automatically populates this representation. The representation can answer compositional questions such as: {``}Which are the best published results reported on the NIST 09 Chinese to English dataset?{''} and {``}What are the most important methods for speeding up phrase-based decoding?{''} Answering such questions usually involves lengthy literature surveys. Current machine reading for academic papers does not usually consider the actual experiments, but mostly focuses on understanding abstracts. We describe annotation work to create an initial hscientific paper; experimental results representationi corpus. The corpus is composed of 67 papers which were manually annotated with a structured representation of experimental results by domain experts. Additionally, we present a baseline algorithm that characterizes the difficulty of the inference task.
Staggered NLP-assisted refinement for Clinical Annotations of Chronic Disease Events
Authors: Stephen Wu, Chung-Il Wi, Sunghwan Sohn, Hongfang Liu, Young Juhn
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Domain-specific annotations for NLP are often centered on real-world applications of text, and incorrect annotations may be particularly unacceptable. In medical text, the process of manual chart review (of a patient{'}s medical record) is error-prone due to its complexity. We propose a staggered NLP-assisted approach to the refinement of clinical annotations, an interactive process that allows initial human judgments to be verified or falsified by means of comparison with an improving NLP system. We show on our internal Asthma Timelines dataset that this approach improves the quality of the human-produced clinical annotations.
``Who was Pietro Badoglio?'' Towards a QA system for Italian History
Authors: Stefano Menini, Rachele Sprugnoli, Antonio Uva
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Question Answering

This paper presents QUANDHO (QUestion ANswering Data for italian HistOry), an Italian question answering dataset created to cover a specific domain, i.e. the history of Italy in the first half of the XX century. The dataset includes questions manually classified and annotated with Lexical Answer Types, and a set of question-answer pairs. This resource, freely available for research purposes, has been used to retrain a domain independent question answering system so to improve its performances in the domain of interest. Ongoing experiments on the development of a question classifier and an automatic tagger of Lexical Answer Types are also presented.
A Document Repository for Social Media and Speech Conversations
Authors: Adam Funk, Robert Gaizauskas, Benoit Favre
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a successfully implemented document repository REST service for flexible SCRUD (search, crate, read, update, delete) storage of social media conversations, using a GATE/TIPSTER-like document object model and providing a query language for document features. This software is currently being used in the SENSEI research project and will be published as open-source software before the project ends. It is, to the best of our knowledge, the first freely available, general purpose data repository to support large-scale multimodal (i.e., speech or text) conversation analytics.
Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse
Authors: Artemis Parvizi, Matt Kohl, Meritxell Gonz{\`a}lez, Roser Saur{\'\i}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chief of which was the capability to losslessly capture diverse features of many different languages in a dictionary format, while supplying a framework for inferring relations like translation, derivation, etc., between the data. Building on valuable features of existing models, and working with OUP monolingual and bilingual dictionary datasets, we have designed and implemented a new linguistic ontology. The ontology has been reviewed by a number of computational linguists, and we are working to move more dictionary data into it. We have also developed APIs to surface the linked data to dictionary websites.
Providing a Catalogue of Language Resources for Commercial Users
Authors: Bente Maegaard, Lina Henriksen, Andrew Joscelyne, Vesna Lusicky, Margaretha Mazura, Sussi Olsen, Claus Povlsen, Philippe Wacker
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

Language resources (LR) are indispensable for the development of tools for machine translation (MT) or various kinds of computer-assisted translation (CAT). In particular language corpora, both parallel and monolingual are considered most important for instance for MT, not only SMT but also hybrid MT. The Language Technology Observatory will provide easy access to information about LRs deemed to be useful for MT and other translation tools through its LR Catalogue. In order to determine what aspects of an LR are useful for MT practitioners, a user study was made, providing a guide to the most relevant metadata and the most relevant quality criteria. We have seen that many resources exist which are useful for MT and similar work, but the majority are for (academic) research or educational use only, and as such not available for commercial use. Our work has revealed a list of gaps: coverage gap, awareness gap, quality gap, quantity gap. The paper ends with recommendations for a forward-looking strategy.