pwc-archive/papers-with-abstracts huggingface dataset:
Sentiment Lexicons for Arabic Social Media
Authors: Saif Mohammad, Mohammad Salameh, Svetlana Kiritchenko
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Sentence, Sentiment Analysis, Translation, valid

Existing Arabic sentiment lexicons have low coverageâ€•with only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We compare the usefulness of new and old sentiment lexicons in the downstream application of sentence-level sentiment analysis. Our baseline sentiment analysis system uses numerous surface form features. Nonetheless, the system benefits from using additional features drawn from sentiment lexicons. The best result is obtained using the automatically generated Dialectal Hashtag Lexicon and the Arabic translations of the NRC Emotion Lexicon (accuracy of 66.6{\%}). Finally, we describe a qualitative study of the automatic translations of English sentiment lexicons into Arabic, which shows that about 88{\%} of the automatically translated entries are valid for English as well. Close to 10{\%} of the invalid entries are caused by gross mistranslations, close to 40{\%} by translations into a related word, and about 50{\%} by differences in how the word is used in Arabic.
A Language Independent Method for Generating Large Scale Polarity Lexicons
Authors: Giuseppe Castellucci, Danilo Croce, Roberto Basili
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentiment Analysis

Sentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts. Many approaches in literature are based on resources that model the prior polarity of words or multi-word expressions, i.e. a polarity lexicon. Such resources are defined by teams of annotators, i.e. a manual annotation is provided to associate emotional or sentiment facets to the lexicon entries. The development of such lexicons is an expensive and language dependent process, making them often not covering all the linguistic sentiment phenomena. Moreover, once a lexicon is defined it can hardly be adopted in a different language or even a different domain. In this paper, we present several Distributional Polarity Lexicons (DPLs), i.e. large-scale polarity lexicons acquired with an unsupervised methodology based on Distributional Models of Lexical Semantics. Given a set of heuristically annotated sentences from Twitter, we transfer the sentiment information from sentences to words. The approach is mostly unsupervised, and experimental evaluations on Sentiment Analysis tasks in two languages show the benefits of the generated resources. The generated DPLs are publicly available in English and Italian.
Sentiment Analysis in Social Networks through Topic modeling
Authors: Debashis Naskar, Sidahmed Mokaddem, Miguel Rebollo, Eva Onaindia
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentiment Analysis

In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) we use ANEW, a lexical dictionary to identify affective emotional feelings associated to a message according to the Russell{'}s model of affection; (2) we design a topic modeling mechanism called Sent{\_}LDA, based on the Latent Dirichlet Allocation (LDA) generative model, which allows us to find the topic distribution in a general conversation and we associate topics with emotions; (3) we detect communities in the network according to the density and frequency of the messages among the users; and (4) we compare the sentiments of the communities by using the Russell{'}s model of affect versus polarity and we measure the extent to which topic distribution strengthen likeness in the sentiments of the users of a community. This works contributes with a topic modeling methodology to analyze the sentiments in conversations that take place in social networks.
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings
Authors: Aitor Garc{\'\i}a Pablos, Montse Cuadros, German Rigau
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentiment Analysis, Word Embeddings

A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus.
Could Speaker, Gender or Age Awareness be beneficial in Speech-based Emotion Recognition?
Authors: Maxim Sidorov, Alex Schmitt, er, Eugene Semenkin, Wolfgang Minker
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Emotion Recognition, Spoken Dialogue Systems

Emotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs). The emotional hypothesis of the current response of an end-user might be utilised by the dialogue manager component in order to change the SDS strategy which could result in a quality enhancement. In this study additional speaker-related information is used to improve the performance of the speech-based ER process. The analysed information is the speaker identity, gender and age of a user. Two schemes are described here, namely, using additional information as an independent variable within the feature vector and creating separate emotional models for each speaker, gender or age-cluster independently. The performances of the proposed approaches were compared against the baseline ER system, where no additional information has been used, on a number of emotional speech corpora of German, English, Japanese and Russian. The study revealed that for some of the corpora the proposed approach significantly outperforms the baseline methods with a relative difference of up to 11.9{\%}.
Discriminative Analysis of Linguistic Features for Typological Study
Authors: Hiroya Takamura, Ryo Nagata, Yoshifumi Kawasaki
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Attribute, Missing Values

We address the task of automatically estimating the missing values of linguistic features by making use of the fact that some linguistic features in typological databases are informative to each other. The questions to address in this work are (i) how much predictive power do features have on the value of another feature? (ii) to what extent can we attribute this predictive power to genealogical or areal factors, as opposed to being provided by tendencies or implicational universals? To address these questions, we conduct a discriminative or predictive analysis on the typological database. Specifically, we use a machine-learning classifier to estimate the value of each feature of each language using the values of the other features, under different choices of training data: all the other languages, or all the other languages except for the ones having the same origin or area with the target language.
POS-tagging of Historical Dutch
Authors: Dieuwke Hupkes, Rens Bod
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Domain Adaptation, POS, POS Tagging

We present a study of the adequacy of current methods that are used for POS-tagging historical Dutch texts, as well as an exploration of the influence of employing different techniques to improve upon the current practice. The main focus of this paper is on (unsupervised) methods that are easily adaptable for different domains without requiring extensive manual input. It was found that modernising the spelling of corpora prior to tagging them with a tagger trained on contemporary Dutch results in a large increase in accuracy, but that spelling normalisation alone is not sufficient to obtain state-of-the-art results. The best results were achieved by training a POS-tagger on a corpus automatically annotated by projecting (automatically assigned) POS-tags via word alignments from a contemporary corpus. This result is promising, as it was reached without including any domain knowledge or context dependencies. We argue that the insights of this study combined with semi-supervised learning techniques for domain adaptation can be used to develop a general-purpose diachronic tagger for Dutch.
A Language Resource of German Errors Written by Children with Dyslexia
Authors: Maria Rauschenberger, Luz Rello, Silke F{\"u}chsel, J{\"o}rg Thomaschewski
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper we present a language resource for German, composed of a list of 1,021 unique errors extracted from a collection of texts written by people with dyslexia. The errors were annotated with a set of linguistic characteristics as well as visual and phonetic features. We present the compilation and the annotation criteria for the different types of dyslexic errors. This language resource has many potential uses since errors written by people with dyslexia reflect their difficulties. For instance, it has already been used to design language exercises to treat dyslexia in German. To the best of our knowledge, this is first resource of this kind in German.
CItA: an L1 Italian Learners Corpus to Study the Development of Writing Competence
Authors: Alessia Barbagli, Pietro Lucisano, Felice Dell{'}Orletta, Simonetta Montemagni, Giulia Venturi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school. The corpus was built in the framework of an interdisciplinary study jointly carried out by computational linguistics and experimental pedagogists and aimed at tracking the development of written language competence over the years and students{'} background information.
If You Even Don't Have a Bit of Bible: Learning Delexicalized POS Taggers
Authors: Zhiwei Yu, David Mare{\v{c}}ek, Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}, Daniel Zeman
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: POS, TAG

Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Various unsupervised and semi-supervised methods have been proposed to tag an unseen language. However, many of them require some partial understanding of the target language because they rely on dictionaries or parallel corpora such as the Bible. In this paper, we propose a different method named delexicalized tagging, for which we only need a raw corpus of the target language. We transfer tagging models trained on annotated corpora of one or more resource-rich languages. We employ language-independent features such as word length, frequency, neighborhood entropy, character classes (alphabetic vs. numeric vs. punctuation) etc. We demonstrate that such features can, to certain extent, serve as predictors of the part of speech, represented by the universal POS tag.
The SpeDial datasets: datasets for Spoken Dialogue Systems analytics
Authors: Jos{\'e} Lopes, Arodami Chorianopoulou, Elisavet Palogiannidi, Helena Moniz, Alberto Abad, Katerina Louka, Elias Iosif, Alex Potamianos, ros
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Spoken Dialogue Systems

The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS{'}s development cycle.
Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification
Authors: Dilafruz Amanova, Volha Petukhova, Dietrich Klakow
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Classification, Dialogue Act Classification, General Classification, Sound Classification

This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user utterances. Annotated dialogue resources, however, differ in various respects: data collection settings and modalities used, dialogue task domains and scenarios (if any) underlying the collection, number and roles of dialogue participants involved and dialogue act annotation schemes applied. The presented study encompasses three phases of data-driven investigation. We, first, assess the importance of various types of features and their combinations for effective cross-domain dialogue act classification. Second, we establish the best predictive model comparing various cross-corpora training settings. Finally, we specify models adaptation procedures and explore late fusion approaches to optimize the overall classification decision taking process. The proposed methodology accounts for empirically motivated and technically sound classification procedures that may reduce annotation and training costs significantly.
Towards a Multi-dimensional Taxonomy of Stories in Dialogue
Authors: Kathryn J. Collins, David Traum
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we present a taxonomy of stories told in dialogue. We based our scheme on prior work analyzing narrative structure and method of telling, relation to storyteller identity, as well as some categories particular to dialogue, such as how the story gets introduced. Our taxonomy currently has 5 major dimensions, with most having sub-dimensions - each dimension has an associated set of dimension-specific labels. We adapted an annotation tool for this taxonomy and have annotated portions of two different dialogue corpora, Switchboard and the Distress Analysis Interview Corpus. We present examples of some of the tags and concepts with stories from Switchboard, and some initial statistics of frequencies of the tags.
PentoRef: A Corpus of Spoken References in Task-oriented Dialogues
Authors: Sina Zarrie{\ss}, Julian Hough, Casey Kennington, Ramesh Manuvinakurike, David DeVault, Raquel Fern{\'a}ndez, David Schlangen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings. The corpus is multilingual, with English and German sections, and overall comprises more than 20000 utterances. The dialogues are fully transcribed and annotated with referring expressions mapped to objects in corresponding visual scenes, which makes the corpus a rich resource for research on spoken referring expressions in generation and resolution. The corpus includes several sub-corpora that correspond to different dialogue situations where parameters related to interactivity, visual access, and verbal channel have been manipulated in systematic ways. The corpus thus lends itself to very targeted studies of reference in spontaneous dialogue.
Transfer of Corpus-Specific Dialogue Act Annotation to ISO Standard: Is it worth it?
Authors: Shammur Absar Chowdhury, Evgeny Stepanov, Giuseppe Riccardi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Classification, Dialogue Act Classification, General Classification

Spoken conversation corpora often adapt existing Dialogue Act (DA) annotation specifications, such as DAMSL, DIT++, etc., to task specific needs, yielding incompatible annotations; thus, limiting corpora re-usability. Recently accepted ISO standard for DA annotation {--} Dialogue Act Markup Language (DiAML) {--} is designed as domain and application independent. Moreover, the clear separation of dialogue dimensions and communicative functions, coupled with the hierarchical organization of the latter, allows for classification at different levels of granularity. However, re-annotating existing corpora with the new scheme might require significant effort. In this paper we test the utility of the ISO standard through comparative evaluation of the corpus-specific legacy and the semi-automatically transferred DiAML DA annotations on supervised dialogue act classification task. To test the domain independence of the resulting annotations, we perform cross-domain and data aggregation evaluation. Compared to the legacy annotation scheme, on the Italian LUNA Human-Human corpus, the DiAML annotation scheme exhibits better cross-domain and data aggregation classification performance, while maintaining comparable in-domain performance.
WikiCoref: An English Coreference-annotated Corpus of Wikipedia Articles
Authors: Abbas Ghaddar, Phillippe Langlais
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, coreference-resolution, Coreference Resolution

This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia. Our annotation scheme follows the one of OntoNotes with a few disparities. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Since most similar annotation efforts concentrate on very specific types of written text, mainly newswire, there is a lack of resources for otherwise over-used Wikipedia texts. The corpus described in this paper addresses this issue. We present a freely available resource we initially devised for improving coreference resolution algorithms dedicated to Wikipedia texts. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation.
Exploitation of Co-reference in Distributional Semantics
Authors: Dominik Schlechtweg
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that similar words have similar contexts. Deducing meaning from the distribution of words is interesting as it can be done automatically on large amounts of freely available raw text. It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e.g., syntactic―information to improve distributional semantic models. In contrast, less attention has been paid to semantic information in the research community. One reason for this is that the extraction of semantic information from raw text is a complex, elaborate matter and in great parts not yet satisfyingly solved. Recently, however, there have been successful attempts to integrate a certain kind of semantic information, i.e., co-reference. Two basically different kinds of information contributed by co-reference with respect to the distribution of words will be identified. We will then focus on one of these and examine its general potential to improve distributional semantic models as well as certain more specific hypotheses.
Adapting an Entity Centric Model for Portuguese Coreference Resolution
Authors: Ev Fonseca, ro, Renata Vieira, Aline Vanin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: coreference-resolution, Coreference Resolution

This paper presents the adaptation of an Entity Centric Model for Portuguese coreference resolution, considering 10 named entity categories. The model was evaluated on named e using the HAREM Portuguese corpus and the results are 81.0{\%} of precision and 58.3{\%} of recall overall, the resulting system is freely available
IMS HotCoref DE: A Data-driven Co-reference Resolver for German
Authors: Ina Roesiger, Jonas Kuhn
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a data-driven co-reference resolution system for German that has been adapted from IMS HotCoref, a co-reference resolver for English. It describes the difficulties when resolving co-reference in German text, the adaptation process and the features designed to address linguistic challenges brought forth by German. We report performance on the reference dataset T{\"u}Ba-D/Z and include a post-task SemEval 2010 evaluation, showing that the resolver achieves state-of-the-art performance. We also include ablation experiments that indicate that integrating linguistic features increases results. The paper also describes the steps and the format necessary to use the resolver on new texts. The tool is freely available for download.
Coreference Annotation Scheme and Relation Types for Hindi
Authors: V Mujadia, an, Palash Gupta, Dipti Misra Sharma
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Relation

This paper describes a coreference annotation scheme, coreference annotation specific issues and their solutions through our proposed annotation scheme for Hindi. We introduce different co-reference relation types between continuous mentions of the same coreference chain such as {``}Part-of{''}, {``}Function-value pair{''} etc. We used Jaccard similarity based Krippendorff{`}s{'} alpha to demonstrate consistency in annotation scheme, annotation and corpora. To ease the coreference annotation process, we built a semi-automatic Coreference Annotation Tool (CAT). We also provide statistics of coreference annotation on Hindi Dependency Treebank (HDTB).
Coreference in Prague Czech-English Dependency Treebank
Authors: Anna Nedoluzhko, Michal Nov{\'a}k, Silvie Cinkov{\'a}, Marie Mikulov{\'a}, Ji{\v{r}}{\'\i} M{\'\i}rovsk{\'y}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present coreference annotation on parallel Czech-English texts of the Prague Czech-English Dependency Treebank (PCEDT). The paper describes innovations made to PCEDT 2.0 concerning coreference, as well as coreference information already present there. We characterize the coreference annotation scheme, give the statistics and compare our annotation with the coreference annotation in Ontonotes and Prague Dependency Treebank for Czech. We also present the experiments made using this corpus to improve the alignment of coreferential expressions, which helps us to collect better statistics of correspondences between types of coreferential relations in Czech and English. The corpus released as PCEDT 2.0 Coref is publicly available.