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"Topic": models, code, and papers

Contrastive Reasons Detection and Clustering from Online Polarized Debate

Aug 01, 2019
Amine Trabelsi, Osmar R. Zaiane

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.

* Best paper award in CICLing 2019: International Conference on Computational Linguistics and Intelligent Text Processing 

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Alquist: The Alexa Prize Socialbot

Apr 18, 2018
Jan Pichl, Petr Marek, Jakub Konrád, Martin Matulík, Hoang Long Nguyen, Jan Šedivý

This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition, and processing, dialogue manager, NLG, knowledge aggregation and hierarchy of sub-dialogs. We present some of the experimental results.


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Basic tasks of sentiment analysis

Oct 18, 2017
Iti Chaturvedi, Soujanya Poria, Erik Cambria

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about.

* Encyclopedia of Social Network Analysis and Mining, 2017 

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SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

Apr 20, 2017
Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, Arkaitz Zubiaga

Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.


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Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron

Sep 22, 2021
A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno

We explore Boccaccio's Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We focus our analysis on the question: Do the different storytellers in the text exhibit distinct personalities? To answer this question, we curate and release a dataset based on the authoritative edition of the text. We use supervised classification methods to predict storytellers based on the stories they tell, confirming the difficulty of the task, and demonstrate that topic modeling can extract thematic storyteller "profiles."

* The 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (co-located with EMNLP 2021) 

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Towards Deconfounding the Influence of Subject's Demographic Characteristics in Question Answering

Apr 15, 2021
Maharshi Gor, Kellie Webster, Jordan Boyd-Graber

Question Answering (QA) tasks are used as benchmarks of general machine intelligence. Therefore, robust QA evaluation is critical, and metrics should indicate how models will answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, models generalize -- we find little evidence that accuracy is lower for people based on gender or nationality. Instead, there is more variation in question topic and question ambiguity. Adequately accessing the generalization of QA systems requires more representative datasets.


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Building a Swedish Open-Domain Conversational Language Model

Apr 12, 2021
Tobias Norlund, Agnes Stenbom

We present on-going work of evaluating the, to our knowledge, first large generative language model trained to converse in Swedish, using data from the online discussion forum Flashback. We conduct a human evaluation pilot study that indicates the model is often able to respond to conversations in both a human-like and informative manner, on a diverse set of topics. While data from online forums can be useful to build conversational systems, we reflect on the negative consequences that incautious application might have, and the need for taking active measures to safeguard against them.


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#LaCulturaNonsiFerma: Report on Use and Diffusion of #Hashtags from the Italian Cultural Institutions during the COVID-19 outbreak

Mar 22, 2021
Carola Carlino, Gennaro Nolano, Maria Pia di Buono, Johanna Monti

This report presents an analysis of #hashtags used by Italian Cultural Heritage institutions to promote and communicate cultural content during the COVID-19 lock-down period in Italy. Several activities to support and engage users' have been proposed using social media. Most of these activities present one or more #hashtags which help to aggregate content and create a community on specific topics. Results show that on one side Italian institutions have been very proactive in adapting to the pandemic scenario and on the other side users' reacted very positively increasing their participation in the proposed activities.

* 17 pages, 14 figures, 5 tables 

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LaCulturaNonSiFerma -- Report su uso e la diffusione degli hashtag delle istituzioni culturali italiane durante il periodo di lockdown

May 21, 2020
Carola Carlino, Gennaro Nolano, Maria Pia di Buono, Johanna Monti

This report presents an analysis of #hashtags used by Italian Cultural Heritage institutions to promote and communicate cultural content during the COVID-19 lock-down period in Italy. Several activities to support and engage users' have been proposed using social media. Most of these activities present one or more #hashtags which help to aggregate content and create a community on specific topics. Results show that on one side Italian institutions have been very proactive in adapting to the pandemic scenario and on the other side users' reacted very positively increasing their participation in the proposed activities.

* in Italian 

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CommentsRadar: Dive into Unique Data on All Comments on the Web

Aug 16, 2019
Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov, Eugene Beloded

We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites. The engine aggregatesarticles and comments for these articles, extracts named entities,links them together and with knowledge base entries, performssentiment analysis, and aggregates the results, aiming to mine fortemporal trends and other insights. In this work, we present thegeneral engine, discuss the models used for all steps of this pipeline,and introduce several case studies that discover important insightsfrom online commenting data.


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