pwc-archive/papers-with-abstracts huggingface dataset:
Gold-standard for Topic-specific Sentiment Analysis of Economic Texts
Authors: Pyry Takala, Pekka Malo, Ankur Sinha, Oskar Ahlgren
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentence, Sentiment Analysis

Public opinion, as measured by media sentiment, can be an important indicator in the financial and economic context. These are domains where traditional sentiment estimation techniques often struggle, and existing annotated sentiment text collections are of less use. Though considerable progress has been made in analyzing sentiments at sentence-level, performing topic-dependent sentiment analysis is still a relatively uncharted territory. The computation of topic-specific sentiments has commonly relied on naive aggregation methods without much consideration to the relevance of the sentences to the given topic. Clearly, the use of such methods leads to a substantial increase in noise-to-signal ratio. To foster development of methods for measuring topic-specific sentiments in documents, we have collected and annotated a corpus of financial news that have been sampled from Thomson Reuters newswire. In this paper, we describe the annotation process and evaluate the quality of the dataset using a number of inter-annotator agreement metrics. The annotations of 297 documents and over 9000 sentences can be used for research purposes when developing methods for detecting topic-wise sentiment in financial text.
Visualization of Language Relations and Families: MultiTree
Authors: Damir Cavar, Malgorzata Cavar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

MultiTree is an NFS-funded project collecting scholarly hypotheses about language relationships, and visualizing them on a web site in the form of trees or graphs. Two open online interfaces allow scholars, students, and the general public an easy access to search for language information or comparisons of competing hypotheses. One objective of the project was to facilitate research in historical linguistics. MultiTree has evolved to a much more powerful tool, it is not just a simple repository of scholarly information. In this paper we present the MultiTree interfaces and the impact of the project beyond the field of historical linguistics, including, among others, the use of standardized ISO language codes, and creating an interconnected database of language and dialect names, codes, publications, and authors. Further, we offer the dissemination of linguistic findings world-wide to both scholars and the general public, thus boosting the collaboration and accelerating the scientific exchange. We discuss also the ways MultiTree will develop beyond the time of the duration of the funding.
HiEve: A Corpus for Extracting Event Hierarchies from News Stories
Authors: Goran Glava{\v{s}}, Jan {\v{S}}najder, Marie-Francine Moens, Parisa Kordjamshidi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Relation Extraction

In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., supereventâ€•subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58{\%} F1-score, only 11{\%} less than the inter annotator agreement.
Image Annotation with ISO-Space: Distinguishing Content from Structure
Authors: James Pustejovsky, Zachary Yocum
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Content-Based Image Retrieval, Image Captioning, Image Retrieval, Semantic Role Labeling

Natural language descriptions of visual media present interesting problems for linguistic annotation of spatial information. This paper explores the use of ISO-Space, an annotation specification to capturing spatial information, for encoding spatial relations mentioned in descriptions of images. Especially, we focus on the distinction between references to representational content and structural components of images, and the utility of such a distinction within a compositional semantics. We also discuss how such a structure-content distinction within the linguistic annotation can be leveraged to compute further inferences about spatial configurations depicted by images with verbal captions. We construct a composition table to relate content-based relations to structure-based relations in the image, as expressed in the captions. While still preliminary, our initial results suggest that a weak composition table is both sound and informative for deriving new spatial relations.
The ETAPE speech processing evaluation
Authors: Olivier Galibert, Jeremy Leixa, Gilles Adda, Khalid Choukri, Guillaume Gravier
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, Speaker Recognition, speech-recognition, Speech Recognition

The ETAPE evaluation is the third evaluation in automatic speech recognition and associated technologies in a series which started with ESTER. This evaluation proposed some new challenges, by proposing TV and radio shows with prepared and spontaneous speech, annotation and evaluation of overlapping speech, a cross-show condition in speaker diarization, and new, complex but very informative named entities in the information extraction task. This paper presents the whole campaign, including the data annotated, the metrics used and the anonymized system results. All the data created in the evaluation, hopefully including system outputs, will be distributed through the ELRA catalogue in the future.
PanLex: Building a Resource for Panlingual Lexical Translation
Authors: David Kamholz, Jonathan Pool, Susan Colowick
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Translation

PanLex, a project of The Long Now Foundation, aims to enable the translation of lexemes among all human languages in the world. By focusing on lexemic translations, rather than grammatical or corpus data, it achieves broader lexical and language coverage than related projects. The PanLex database currently documents 20 million lexemes in about 9,000 language varieties, with 1.1 billion pairwise translations. The project primarily engages in content procurement, while encouraging outside use of its data for research and development. Its data acquisition strategy emphasizes broad, high-quality lexical and language coverage. The project plans to add data derived from 4,000 new sources to the database by the end of 2016. The dataset is publicly accessible via an HTTP API and monthly snapshots in CSV, JSON, and XML formats. Several online applications have been developed that query PanLex data. More broadly, the project aims to make a contribution to the preservation of global linguistic diversity.
Large SMT data-sets extracted from Wikipedia
Authors: Dan Tufi{\c{s}}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Domain Adaptation, Machine Translation, Sentence, Translation

The article presents experiments on mining Wikipedia for extracting SMT useful sentence pairs in three language pairs. Each extracted sentence pair is associated with a cross-lingual lexical similarity score based on which, several evaluations have been conducted to estimate the similarity thresholds which allow the extraction of the most useful data for training three-language pairs SMT systems. The experiments showed that for a similarity score higher than 0.7 all sentence pairs in the three language pairs were fully parallel. However, including in the training sets less parallel sentence pairs (that is with a lower similarity score) showed significant improvements in the translation quality (BLEU-based evaluations). The optimized SMT systems were evaluated on unseen test-sets also extracted from Wikipedia. As one of the main goals of our work was to help Wikipedia contributors to translate (with as little post editing as possible) new articles from major languages into less resourced languages and vice-versa, we call this type of translation experiments in-genre translation. As in the case of in-domain translation, our evaluations showed that using only in-genre training data for translating same genre new texts is better than mixing the training data with out-of-genre (even) parallel texts.
NomLex-PT: A Lexicon of Portuguese Nominalizations
Authors: Valeria de Paiva, Livy Real, Alex Rademaker, re, Gerard de Melo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents NomLex-PT, a lexical resource describing Portuguese nominalizations. NomLex-PT connects verbs to their nominalizations, thereby enabling NLP systems to observe the potential semantic relationships between the two words when analysing a text. NomLex-PT is freely available and encoded in RDF for easy integration with other resources. Most notably, we have integrated NomLex-PT with OpenWordNet-PT, an open Portuguese Wordnet.
VERTa: Facing a Multilingual Experience of a Linguistically-based MT Evaluation
Authors: Elisabet Comelles, Jordi Atserias, Victoria Arranz, Irene Castell{\'o}n, Jordi Ses{\'e}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentiment Analysis, Translation

There are several MT metrics used to evaluate translation into Spanish, although most of them use partial or little linguistic information. In this paper we present the multilingual capability of VERTa, an automatic MT metric that combines linguistic information at lexical, morphological, syntactic and semantic level. In the experiments conducted we aim at identifying those linguistic features that prove the most effective to evaluate adequacy in Spanish segments. This linguistic information is tested both as independent modules (to observe what each type of feature provides) and in a combinatory fastion (where different kinds of information interact with each other). This allows us to extract the optimal combination. In addition we compare these linguistic features to those used in previous versions of VERTa aimed at evaluating adequacy for English segments. Finally, experiments show that VERTa can be easily adapted to other languages than English and that its collaborative approach correlates better with human judgements on adequacy than other well-known metrics.
Corpus and Method for Identifying Citations in Non-Academic Text
Authors: Yifan He, Adam Meyers
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles

We attempt to identify citations in non-academic text such as patents. Unlike academic articles which often provide bibliographies and follow consistent citation styles, non-academic text cites scientific research in a more ad-hoc manner. We manually annotate citations in 50 patents, train a CRF classifier to find new citations, and apply a reranker to incorporate non-local information. Our best system achieves 0.83 F-score on 5-fold cross validation.
Building a Dataset for Summarization and Keyword Extraction from Emails
Authors: Vanessa Loza, Shibamouli Lahiri, Rada Mihalcea, Po-Hsiang Lai
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Abstractive Text Summarization, Keyword Extraction

This paper introduces a new email dataset, consisting of both single and thread emails, manually annotated with summaries and keywords. A total of 349 emails and threads have been annotated. The dataset is our first step toward developing automatic methods for summarization and keyword extraction from emails. We describe the email corpus, along with the annotation interface, annotator guidelines, and agreement studies.
Improving Open Relation Extraction via Sentence Re-Structuring
Authors: Jordan Schmidek, Denilson Barbosa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Named Entity Recognition (NER), Relation, Relation Extraction, Sentence

Information Extraction is an important task in Natural Language Processing, consisting of finding a structured representation for the information expressed in natural language text. Two key steps in information extraction are identifying the entities mentioned in the text, and the relations among those entities. In the context of Information Extraction for the World Wide Web, unsupervised relation extraction methods, also called Open Relation Extraction (ORE) systems, have become prevalent, due to their effectiveness without domain-specific training data. In general, these systems exploit part-of-speech tags or semantic information from the sentences to determine whether or not a relation exists, and if so, its predicate. This paper discusses some of the issues that arise when even moderately complex sentences are fed into ORE systems. A process for re-structuring such sentences is discussed and evaluated. The proposed approach replaces complex sentences by several others that, together, convey the same meaning and are more amenable to extraction by current ORE systems. The results of an experimental evaluation show that this approach succeeds in reducing the processing time and increasing the accuracy of the state-of-the-art ORE systems.
How to Use less Features and Reach Better Performance in Author Gender Identification
Authors: Juan Soler Company, Leo Wanner
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Author Profiling, Dimensionality Reduction, Feature Engineering, Marketing

Over the last years, author profiling in general and author gender identification in particular have become a popular research area due to their potential attractive applications that range from forensic investigations to online marketing studies. However, nearly all state-of-the-art works in the area still very much depend on the datasets they were trained and tested on, since they heavily draw on content features, mostly a large number of recurrent words or combinations of words extracted from the training sets. We show that using a small number of features that mainly depend on the structure of the texts we can outperform other approaches that depend mainly on the content of the texts and that use a huge number of features in the process of identifying if the author of a text is a man or a woman. Our system has been tested against a dataset constructed for our work as well as against two datasets that were previously used in other papers.
Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet
Authors: Michael Mohler, Marc Tomlinson, David Bracewell, Bryan Rink
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Image Retrieval, Information Retrieval, Semantic Textual Similarity, Text Generation, Text Simplification, Word Sense Disambiguation

In this work, we present two complementary methods for the expansion of psycholinguistics norms. The first method is a random-traversal spreading activation approach which transfers existing norms onto semantically related terms using notions of synonymy, hypernymy, and pertainymy to approach full coverage of the English language. The second method makes use of recent advances in distributional similarity representation to transfer existing norms to their closest neighbors in a high-dimensional vector space. These two methods (along with a naive hybrid approach combining the two) have been shown to significantly outperform a state-of-the-art resource expansion system at our pilot task of imageability expansion. We have evaluated these systems in a cross-validation experiment using 8,188 norms found in existing pscholinguistics literature. We have also validated the quality of these combined norms by performing a small study using Amazon Mechanical Turk (AMT).
Languagesindanger.eu - Including Multimedia Language Resources to disseminate Knowledge and Create Educational Material on less-Resourced Languages
Authors: Dagmar Jung, Katarzyna Klessa, Zsuzsa Duray, Beatrix Oszk{\'o}, M{\'a}ria Sipos, S{\'a}ndor Szever{\'e}nyi, Zsuzsa V{\'a}rnai, Paul Trilsbeek, Tam{\'a}s V{\'a}radi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity

The present paper describes the development of the languagesindanger.eu interactive website as an example of including multimedia language resources to{\^A} disseminate knowledge and create educational material on{\^A} less-resourced languages. The website is a product of INNET (Innovative networking in infrastructure for endangered languages), European FP7 project. Its main functions can be summarized as related to the three following areas: (1) raising students{'} awareness of language endangerment and arouse their interest in linguistic diversity, language maintenance and language documentation; (2) informing both students and teachers about these topics and show ways how they can enlarge their knowledge further with a special emphasis on information about language archives; (3) helping teachers include these topics into their classes. The website has been localized into five language versions with the intention to be accessible to both scientific and non-scientific communities such as (primarily) secondary school teachers and students, beginning university students of linguistics, journalists, the interested public, and also members of speech communities who speak minority languages.
A Cross-language Corpus for Studying the Phonetics and Phonology of Prominence
Authors: Bistra Andreeva, William Barry, Jacques Koreman
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The present article describes a corpus which was collected for the cross-language comparison of prominence. In the data analysis, the acoustic-phonetic properties of words spoken with two different levels of accentuation (de-accented and nuclear accented in non-contrastive narrow-focus) are examined in question-answer elicited sentences and iterative imitations (on the syllable da) produced by Bulgarian, Russian, French, German and Norwegian speakers (3 male and 3 female per language). Normalized parameter values allow a comparison of the properties employed in differentiating the two levels of accentuation. Across the five languages there are systematic differences in the degree to which duration, f0, intensity and spectral vowel definition change with changing prominence under different focus conditions. The link with phonological differences between the languages is discussed.
DeLex, a freely-avaible, large-scale and linguistically grounded morphological lexicon for German
Authors: Beno{\^\i}t Sagot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis, Morphological Inflection

We introduce DeLex, a freely-avaible, large-scale and linguistically grounded morphological lexicon for German developed within the Alexina framework. We extracted lexical information from the German wiktionary and developed a morphological inflection grammar for German, based on a linguistically sound model of inflectional morphology. Although the developement of DeLex involved some manual work, we show that is represents a good tradeoff between development cost, lexical coverage and resource accuracy.
Using Resource-Rich Languages to Improve Morphological Analysis of Under-Resourced Languages
Authors: Peter Baumann, Janet Pierrehumbert
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Lemmatization, Morphological Analysis

The world-wide proliferation of digital communications has created the need for language and speech processing systems for under-resourced languages. Developing such systems is challenging if only small data sets are available, and the problem is exacerbated for languages with highly productive morphology. However, many under-resourced languages are spoken in multi-lingual environments together with at least one resource-rich language and thus have numerous borrowings from resource-rich languages. Based on this insight, we argue that readily available resources from resource-rich languages can be used to bootstrap the morphological analyses of under-resourced languages with complex and productive morphological systems. In a case study of two such languages, Tagalog and Zulu, we show that an easily obtainable English wordlist can be deployed to seed a morphological analysis algorithm from a small training set of conversational transcripts. Our method achieves a precision of 100{\%} and identifies 28 and 66 of the most productive affixes in Tagalog and Zulu, respectively.
Accommodations in Tuscany as Linked Data
Authors: Clara Bacciu, Angelica Lo Duca, Andrea Marchetti, Maurizio Tesconi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Entity Disambiguation, Named Entity Recognition (NER), Sentiment Analysis

The OpeNER Linked Dataset (OLD) contains 19.140 entries about accommodations in Tuscany (Italy). For each accommodation, it describes the type, e.g. hotel, bed and breakfast, hostel, camping etc., and other useful information, such as a short description, the Web address, its location and the features it provides. OLD is the linked data version of the open dataset provided by Fondazione Sistema Toscana, the representative system for tourism in Tuscany. In addition, to the original dataset, OLD provides also the link of each accommodation to the most common social media (Facebook, Foursquare, Google Places and Booking). OLD exploits three common ontologies of the accommodation domain: Acco, Hontology and GoodRelations. The idea is to provide a flexible dataset, which speaks more than one ontology. OLD is available as a SPARQL node and is released under the Creative Commons release. Finally, OLD is developed within the OpeNER European project, which aims at building a set of ready to use tools to recognize and disambiguate entity mentions and perform sentiment analysis and opinion detection on texts. Within the project, OLD provides a named entity repository for entity disambiguation.
The DWAN framework: Application of a web annotation framework for the general humanities to the domain of language resources
Authors: Przemyslaw Lenkiewicz, Olha Shkaravska, Twan Goosen, Daan Broeder, Menzo Windhouwer, Stephanie Roth, Olof Olsson
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Researchers share large amounts of digital resources, which offer new chances for cooperation. Collaborative annotation systems are meant to support this. Often these systems are targeted at a specific task or domain, e.g., annotation of a corpus. The DWAN framework for web annotation is generic and can support a wide range of tasks and domains. A key feature of the framework is its support for caching representations of the annotated resource. This allows showing the context of the annotation even if the resource has changed or has been removed. The paper describes the design and implementation of the framework. Use cases provided by researchers are well in line with the key characteristics of the DWAN annotation framework.