pwc-archive/papers-with-abstracts huggingface dataset:
Personality Estimation from Japanese Text
Authors: Koichi Kamijo, Tetsuya Nasukawa, Hideya Kitamura
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We created a model to estimate personality trait from authors{'} text written in Japanese and measured its performance by conducting surveys and analyzing the Twitter data of 1,630 users. We used the Big Five personality traits for personality trait estimation. Our approach is a combination of category- and Word2Vec-based approaches. For the category-based element, we added several unique Japanese categories along with the ones regularly used in the English model, and for the Word2Vec-based element, we used a model called GloVe. We found that some of the newly added categories have a stronger correlation with personality traits than other categories do and that the combination of the category- and Word2Vec-based approaches improves the accuracy of the personality trait estimation compared with the case of using just one of them.
Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters
Authors: Fabio Celli, Evgeny Stepanov, Massimo Poesio, Giuseppe Riccardi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification, Opinion Mining, Sentiment Analysis

On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.
Sarcasm Detection : Building a Contextual Hierarchy
Authors: Taradheesh Bali, Navjyoti Singh
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Lexical Analysis, Sarcasm Detection, Sentiment Analysis

The conundrum of understanding and classifying sarcasm has been dealt with by the traditional theorists as an analysis of a sarcastic utterance and the ironic situation that surrounds it. The problem with such an approach is that it is too narrow, as it is unable to sufficiently utilize the two indispensable agents in making such an utterance, viz. the speaker and the listener. It undermines the necessary context required to comprehend a sarcastic utterance. In this paper, we propose a novel approach towards understanding sarcasm in terms of the existing knowledge hierarchy between the two participants, which forms the basis of the context that both agents share. The difference in relationship of the speaker of the sarcastic utterance and the disparate audience found on social media, such as Twitter, is also captured. We then apply our model on a corpus of tweets to achieve significant results and consequently, shed light on subjective nature of context, which is contingent on the relation between the speaker and the listener.
Social and linguistic behavior and its correlation to trait empathy
Authors: Marina Litvak, Jahna Otterbacher, Chee Siang Ang, David Atkins
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: regression

A growing body of research exploits social media behaviors to gauge psychological character-istics, though trait empathy has received little attention. Because of its intimate link to the abil-ity to relate to others, our research aims to predict participants{'} levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari-ance explained in Davis{'} Interpersonal Reactivity Index (IRI) scores on four constructs (em-pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy{'}s role in facilitating interactions on social media.
The Challenges of Multi-dimensional Sentiment Analysis Across Languages
Authors: Emily {\"O}hman, Timo Honkela, J{\"o}rg Tiedemann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Sentiment Analysis, Translation

This paper outlines a pilot study on multi-dimensional and multilingual sentiment analysis of social media content. We use parallel corpora of movie subtitles as a proxy for colloquial language in social media channels and a multilingual emotion lexicon for fine-grained sentiment analyses. Parallel data sets make it possible to study the preservation of sentiments and emotions in translation and our assessment reveals that the lexical approach shows great inter-language agreement. However, our manual evaluation also suggests that the use of purely lexical methods is limited and further studies are necessary to pinpoint the cross-lingual differences and to develop better sentiment classifiers.
The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems
Authors: Firoj Alam, Fabio Celli, Evgeny A. Stepanov, Arindam Ghosh, Giuseppe Riccardi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Articles, Prediction

In this paper, we address the issue of automatic prediction of readers{'} mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.
Microblog Emotion Classification by Computing Similarity in Text, Time, and Space
Authors: Anja Summa, Bernd Resch, Michael Strube
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Emotion Classification, General Classification, Sentiment Analysis

Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.
A domain-agnostic approach for opinion prediction on speech
Authors: Pedro Bispo Santos, Lisa Beinborn, Iryna Gurevych
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Emotion Recognition, Feature Engineering, Opinion Mining, Speaker Identification

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state-of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.
Can We Make Computers Laugh at Talks?
Authors: Chong Min Lee, Su-Youn Yoon, Lei Chen
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Considering the importance of public speech skills, a system which makes a prediction on where audiences laugh in a talk can be helpful to a person who prepares for a talk. We investigated a possibility that a state-of-the-art humor recognition system can be used in detecting sentences inducing laughters in talks. In this study, we used TED talks and laughters in the talks as data. Our results showed that the state-of-the-art system needs to be improved in order to be used in a practical application. In addition, our analysis showed that classifying humorous sentences in talks is very challenging due to close distance between humorous and non-humorous sentences.
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
Authors: Danielle L. Mowery, Albert Park, Craig Bryan, Mike Conway
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Major depressive disorder, a debilitating and burdensome disease experienced by individuals worldwide, can be defined by several depressive symptoms (e.g., anhedonia (inability to feel pleasure), depressed mood, difficulty concentrating, etc.). Individuals often discuss their experiences with depression symptoms on public social media platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression. If there was evidence of depression, we then classified whether the tweet contained a depressive symptom and if so, which of three subtypes: depressed mood, disturbed sleep, or fatigue or loss of energy. We observed that the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression (85), evidence of depression (52), and depressive symptoms (49). We report moderate F1-scores for depressive symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to 35 (depressed mood). Our work demonstrates baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder.
Using Wikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering
Authors: Po-Chun Chen, Meng-Jie Zhuang, Chuan-Jie Lin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Named Entity Recognition (NER), Question Answering

This paper proposes a new idea that uses Wikipedia categories as answer types and defines candidate sets inside Wikipedia. The focus of a given question is searched in the hierarchy of Wikipedia main pages. Our searching strategy combines head-noun matching and synonym matching provided in semantic resources. The set of answer candidates is determined by the entry hierarchy in Wikipedia and the hyponymy hierarchy in WordNet. The experimental results show that the approach can find candidate sets in a smaller size but achieve better performance especially for ARTIFACT and ORGANIZATION types, where the performance is better than state-of-the-art Chinese factoid QA systems.
Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System
Authors: Naoki Otani, Daisuke Kawahara, Sadao Kurohashi, Nobuhiro Kaji, Manabu Sassano
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Common Sense Reasoning, Question Answering

Commonsense knowledge is essential for fully understanding language in many situations. We acquire large-scale commonsense knowledge from humans using a game with a purpose (GWAP) developed on a smartphone spoken dialogue system. We transform the manual knowledge acquisition process into an enjoyable quiz game and have collected over 150,000 unique commonsense facts by gathering the data of more than 70,000 players over eight months. In this paper, we present a simple method for maintaining the quality of acquired knowledge and an empirical analysis of the knowledge acquisition process. To the best of our knowledge, this is the first work to collect large-scale knowledge via a GWAP on a widely-used spoken dialogue system.
A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences
Authors: Yukinori Homma, Kugatsu Sadamitsu, Kyosuke Nishida, Ryuichiro Higashinaka, Hisako Asano, Yoshihiro Matsuo
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Attribute, General Classification, Product Recommendation, Question Answering, Sentence, Sentence Classification

This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.
Combining Lexical and Semantic-based Features for Answer Sentence Selection
Authors: Jing Shi, Jiaming Xu, Yiqun Yao, Suncong Zheng, Bo Xu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, Open-Domain Question Answering, Question Answering, Sentence

Question answering is always an attractive and challenging task in natural language processing area. There are some open domain question answering systems, such as IBM Waston, which take the unstructured text data as input, in some ways of humanlike thinking process and a mode of artificial intelligence. At the conference on Natural Language Processing and Chinese Computing (NLPCC) 2016, China Computer Federation hosted a shared task evaluation about Open Domain Question Answering. We achieve the 2nd place at the document-based subtask. In this paper, we present our solution, which consists of feature engineering in lexical and semantic aspects and model training methods. As the result of the evaluation shows, our solution provides a valuable and brief model which could be used in modelling question answering or sentence semantic relevance. We hope our solution would contribute to this vast and significant task with some heuristic thinking.
An Entity-Based approach to Answering Recurrent and Non-Recurrent Questions with Past Answers
Authors: Anietie Andy, Mugizi Rwebangira, Satoshi Sekine
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Community Question Answering, Entity Linking, Question Answering

An Entity-based approach to Answering recurrent and non-recurrent questions with Past Answers Abstract Community question answering (CQA) systems such as Yahoo! Answers allow registered-users to ask and answer questions in various question categories. However, a significant percentage of asked questions in Yahoo! Answers are unanswered. In this paper, we propose to reduce this percentage by reusing answers to past resolved questions from the site. Specifically, we propose to satisfy unanswered questions in entity rich categories by searching for and reusing the best answers to past resolved questions with shared needs. For unanswered questions that do not have a past resolved question with a shared need, we propose to use the best answer to a past resolved question with similar needs. Our experiments on a Yahoo! Answers dataset shows that our approach retrieves most of the past resolved questions that have shared and similar needs to unanswered questions.
BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions
Authors: Mariana Neves, Milena Kraus
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Part-Of-Speech Tagging, Question Answering, Semantic Role Labeling, Vocal Bursts Type Prediction

Question answering (QA) systems need to provide exact answers for the questions that are posed to the system. However, this can only be achieved through a precise processing of the question. During this procedure, one important step is the detection of the expected type of answer that the system should provide by extracting the headword of the questions and identifying its semantic type. We have annotated the headword and assigned UMLS semantic types to 643 factoid/list questions from the BioASQ training data. We present statistics on the corpus and a preliminary evaluation in baseline experiments. We also discuss the challenges on both the manual annotation and the automatic detection of the headwords and the semantic types. We believe that this is a valuable resource for both training and evaluation of biomedical QA systems. The corpus is available at: \url{https://github.com/mariananeves/BioMedLAT}.
Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a Wikipedia-based Robot Application
Authors: Kristiina Jokinen, Graham Wilcock
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Articles, Chatbot, Goal-Oriented Dialogue Systems, Question Answering

The paper describes topic shifting in dialogues with a robot that provides information from Wiki-pedia. The work focuses on a double topical construction of dialogue coherence which refers to discourse coherence on two levels: the evolution of dialogue topics via the interaction between the user and the robot system, and the creation of discourse topics via the content of the Wiki-pedia article itself. The user selects topics that are of interest to her, and the system builds a list of potential topics, anticipated to be the next topic, by the links in the article and by the keywords extracted from the article. The described system deals with Wikipedia articles, but could easily be adapted to other digital information providing systems.
Pairing Wikipedia Articles Across Languages
Authors: Marcus Klang, Pierre Nugues
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Articles, Question Answering, Word Alignment

Wikipedia has become a reference knowledge source for scores of NLP applications. One of its invaluable features lies in its multilingual nature, where articles on a same entity or concept can have from one to more than 200 different versions. The interlinking of language versions in Wikipedia has undergone a major renewal with the advent of Wikidata, a unified scheme to identify entities and their properties using unique numbers. However, as the interlinking is still manually carried out by thousands of editors across the globe, errors may creep in the assignment of entities. In this paper, we describe an optimization technique to match automatically language versions of articles, and hence entities, that is only based on bags of words and anchors. We created a dataset of all the articles on persons we extracted from Wikipedia in six languages: English, French, German, Russian, Spanish, and Swedish. We report a correct match of at least 94.3{\%} on each pair.
SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning
Authors: Sangha Nam, Gyu-Hyeon Choi, Younggyun Hahm, Key-Sun Choi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dependency Parsing, Knowledge Graphs, Open Information Extraction, Question Answering, Reading Comprehension, Sentence

In this paper, we present an open information extraction system so-called SRDF that generates lexical knowledge graphs from unstructured texts. In semantic web, knowledge is expressed in the RDF triple form but the natural language text consist of multiple relations between arguments. For this reason, we combine open information extraction with the reification for the full text extraction to preserve meaning of sentence in our knowledge graph. And also our knowledge graph is designed to adapt for many existing semantic web applications. At the end of this paper, we introduce the result of the experiment and a Korean template generation module developed using SRDF.
QAF: Frame Semantics-based Question Interpretation
Authors: Younggyun Hahm, Sangha Nam, Key-Sun Choi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Knowledge Base Question Answering, Question Answering, Reading Comprehension, Relation, Semantic Parsing

Natural language questions are interpreted to a sequence of patterns to be matched with instances of patterns in a knowledge base (KB) for answering. A natural language (NL) question answering (QA) system utilizes meaningful patterns matching the syntac-tic/lexical features between the NL questions and KB. In the most of KBs, there are only binary relations in triple form to represent relation between two entities or entity and a value using the domain specific ontology. However, the binary relation representation is not enough to cover complex information in questions, and the ontology vocabulary sometimes does not cover the lexical meaning in questions. Complex meaning needs a knowledge representation to link the binary relation-type triples in KB. In this paper, we propose a frame semantics-based semantic parsing approach as KB-independent question pre-processing. We will propose requirements of question interpretation in the KBQA perspective, and a query form representation based on our proposed format QAF (Ques-tion Answering with the Frame Semantics), which is supposed to cover the requirements. In QAF, frame semantics roles as a model to represent complex information in questions and to disambiguate the lexical meaning in questions to match with the ontology vocabu-lary. Our system takes a question as an input and outputs QAF-query by the process which assigns semantic information in the question to its corresponding frame semantic structure using the semantic parsing rules.
Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations
Authors: Yoshinobu Kano
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Question Answering

Answering yes{--}no questions is more difficult than simply retrieving ranked search results. To answer yes{--}no questions, especially when the correct answer is no, one must find an objectionable keyword that makes the question{'}s answer no. Existing systems, such as factoid-based ones, cannot answer yes{--}no questions very well because of insufficient handling of such objectionable keywords. We suggest an algorithm that answers yes{--}no questions by assigning an importance to objectionable keywords. Concretely speaking, we suggest a penalized scoring method that finds and makes lower score for parts of documents that include such objectionable keywords. We check a keyword distribution for each part of a document such as a paragraph, calculating the keyword density as a basic score. Then we use an objectionable keyword penalty when a keyword does not appear in a target part but appears in other parts of the document. Our algorithm is robust for open domain problems because it requires no training. We achieved 4.45 point better results in F1 scores than the best score of the NTCIR-10 RITE2 shared task, also obtained the best score in 2014 mock university examination challenge of the Todai Robot project.