pwc-archive/papers-with-abstracts huggingface dataset:
A Case Study on Learning a Unified Encoder of Relations
Authors: Lisheng Fu, Bonan Min, Thien Huu Nguyen, Ralph Grishman
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Knowledge Base Population, Relation, Relation Extraction

Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.
Convolutions Are All You Need (For Classifying Character Sequences)
Authors: Zach Wood-Doughty, Nicholas Andrews, Mark Dredze
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: All, Classification, Document Classification, General Classification, Language Modeling, Language Modelling, Machine Translation, text-classification, Text Classification

While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.
Step or Not: Discriminator for The Real Instructions in User-generated Recipes
Authors: Shintaro Inuzuka, Takahiko Ito, Jun Harashima
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

In a recipe sharing service, users publish recipe instructions in the form of a series of steps. However, some of the {``}steps{''} are not actually part of the cooking process. Specifically, advertisements of recipes themselves (e.g., {``}introduced on TV{''}) and comments (e.g., {``}Thanks for many messages{''}) may often be included in the step section of the recipe, like the recipe author{'}s communication tool. However, such \textit{fake} steps can cause problems when using recipe search indexing or when being spoken by devices such as smart speakers. As presented in this talk, we have constructed a discriminator that distinguishes between such a fake step and the step actually used for cooking. This project includes, but is not limited to, the creation of annotation data by classifying and analyzing recipe steps and the construction of identification models. Our models use only text information to identify the step. In our test, machine learning models achieved higher accuracy than rule-based methods that use manually chosen clue words.
Combining Human and Machine Transcriptions on the Zooniverse Platform
Authors: Daniel Hanson, Andrea Simenstad
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Transcribing handwritten documents to create fully searchable texts is an essential part of the archival process. Traditional text recognition methods, such as optical character recognition (OCR), do not work on handwritten documents due to their frequent noisiness and OCR{'}s need for individually segmented letters. Crowdsourcing and improved machine models are two modern methods for transcribing handwritten documents.
Identifying Affective Events and the Reasons for their Polarity
Authors: Ellen Riloff
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sarcasm Detection, Weakly-supervised Learning

Many events have a positive or negative impact on our lives (e.g., {``}I bought a house{''} is typically good news, but {''}My house burned down{''} is bad news). Recognizing events that have affective polarity is essential for narrative text understanding, conversational dialogue, and applications such as summarization and sarcasm detection. We will discuss our recent work on identifying affective events and categorizing them based on the underlying reasons for their affective polarity. First, we will describe a weakly supervised learning method to induce a large set of affective events from a text corpus by optimizing for semantic consistency. Second, we will present models to classify affective events based on Human Need Categories, which often explain people{'}s motivations and desires. Our best results use a co-training model that consists of event expression and event context classifiers and exploits both labeled and unlabeled texts. We will conclude with a discussion of interesting directions for future work in this area.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings
Authors: Yuqi Sun, Haoyue Shi, Junfeng Hu
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: TAG, Word Embeddings

In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of \textit{pseudo multi-senses} can be eliminated by linear transformations. In this paper, we show that \textit{pseudo multi-senses} may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate \textit{pseudo multi-senses} in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings
Authors: Carl Saroufim, Akram Almatarky, Mohammad Abdel Hady
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Document Classification, General Classification, Sentiment Analysis, Sentiment Classification, Transfer Learning, Word Embeddings

Data annotation is a critical step to train a text model but it is tedious, expensive and time-consuming. We present a language independent method to train a sentiment polarity model with limited amount of manually-labeled data. Word embeddings such as Word2Vec are efficient at incorporating semantic and syntactic properties of words, yielding good results for document classification. However, these embeddings might map words with opposite polarities, to vectors close to each other. We train Sentiment Specific Word Embeddings (SSWE) on top of an unsupervised Word2Vec model, using either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) on data auto-labeled as {``}Positive{''} or {``}Negative{''}. For this task, we rely on the universality of emojis and emoticons to auto-label a large number of French tweets using a small set of positive and negative emojis and emoticons. Finally, we apply a transfer learning approach to refine the network weights with a small-size manually-labeled training data set. Experiments are conducted to evaluate the performance of this approach on French sentiment classification using benchmark data sets from SemEval 2016 competition. We were able to achieve a performance improvement by using SSWE over Word2Vec. We also used a graph-based approach for label propagation to auto-generate a sentiment lexicon.
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation
Authors: Emily {\"O}hman, Kaisla Kajava, J{\"o}rg Tiedemann, Timo Honkela
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sentiment Analysis

This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, \textit{Sentimentator}, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.
Sentiment analysis under temporal shift
Authors: Jan Lukes, Anders S{\o}gaard
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: feature selection, Sentiment Analysis

Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly strong in sparse models relying only on highly predictive features. Using predictive feature selection, we are able to significantly improve the accuracy of such models over time.
Not Just Depressed: Bipolar Disorder Prediction on Reddit
Authors: Ivan Sekulic, Matej Gjurkovi{\'c}, Jan {\v{S}}najder
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Prediction

Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users{'} self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86{\%}. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.
Saying no but meaning yes: negation and sentiment analysis in Basque
Authors: Jon Alkorta, Koldo Gojenola, Mikel Iruskieta
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Negation, Sentiment Analysis

In this work, we have analyzed the effects of negation on the semantic orientation in Basque. The analysis shows that negation markers can strengthen, weaken or have no effect on sentiment orientation of a word or a group of words. Using the Constraint Grammar formalism, we have designed and evaluated a set of linguistic rules to formalize these three phenomena. The results show that two phenomena, strengthening and no change, have been identified accurately and the third one, weakening, with acceptable results.
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis
Authors: Rong Xiang, Yunfei Long, Qin Lu, Dan Xiong, I-Hsuan Chen
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, Emotion Recognition, General Classification

Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model.
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification
Authors: Tobias Daudert, Paul Buitelaar
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

Social media{'}s popularity in society and research is gaining momentum and simultaneously increasing the importance of short textual content such as microblogs. Microblogs are affected by many factors including the news media, therefore, we exploit sentiments conveyed from news to detect and classify sentiment in microblogs. Given that texts can deal with the same entity but might not be vastly related when it comes to sentiment, it becomes necessary to introduce further measures ensuring the relatedness of texts while leveraging the contained sentiments. This paper describes ongoing research introducing distributional semantics to improve the exploitation of news-contained sentiment to enhance microblog sentiment classification.
Aspect Based Sentiment Analysis into the Wild
Authors: Caroline Brun, Vassilina Nikoulina
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

In this paper, we test state-of-the-art Aspect Based Sentiment Analysis (ABSA) systems trained on a widely used dataset on actual data. We created a new manually annotated dataset of user generated data from the same domain as the training dataset, but from other sources and analyse the differences between the new and the standard ABSA dataset. We then analyse the results in performance of different versions of the same system on both datasets. We also propose light adaptation methods to increase system robustness.
The Role of Emotions in Native Language Identification
Authors: Ilia Markov, Vivi Nastase, Carlo Strapparava, Grigori Sidorov
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Deception Detection, Language Identification, Native Language Identification, Sentiment Analysis

We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language. To check the role of emotions in native language identification (NLI), we model emotion information through polarity and emotion load features, and use document representations using these features to classify the native language of the author. The results indicate that emotion is relevant for NLI, even for high proficiency levels and across topics.
Sentiment Expression Boundaries in Sentiment Polarity Classification
Authors: Rasoul Kaljahi, Jennifer Foster
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Multi-Task Learning, Sentiment Analysis

We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.
Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning
Authors: Ramit Sawhney, Manch, Prachi a, Puneet Mathur, Rajiv Shah, Raj Singh
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, Deep Learning, General Classification, Sentence, Sentence Classification, text-classification, Text Classification

The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.
UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis
Authors: Gustavo Paetzold
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Recognition

We introduce the UTFPR system for the Implicit Emotions Shared Task of 2018: A compositional character-to-word recurrent neural network that does not exploit heavy and/or hard-to-obtain resources. We find that our approach can outperform multiple baselines, and offers an elegant and effective solution to the problem of orthographic variance in tweets.
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition
Authors: Behzad Naderalvojoud, Alaettin Ucan, Ebru Akcapinar Sezer
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Recognition, Feature Engineering, Word Sense Disambiguation

This paper describes the approaches used in HUMIR system for the WASSA-2018 shared task on the implicit emotion recognition. The objective of this task is to predict the emotion expressed by the target word that has been excluded from the given tweet. We suppose this task as a word sense disambiguation in which the target word is considered as a synthetic word that can express 6 emotions depending on the context. To predict the correct emotion, we propose a deep neural network model that uses two BiLSTM networks to represent the contexts in the left and right sides of the target word. The BiLSTM outputs achieved from the left and right contexts are considered as context-sensitive features. These features are used in a feed-forward neural network to predict the target word emotion. Besides this approach, we also combine the BiLSTM model with lexicon-based and emotion-based features. Finally, we employ all models in the final system using Bagging ensemble method. We achieved macro F-measure value of 68.8 on the official test set and ranked sixth out of 30 participants.
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification
Authors: Qimin Zhou, Hao Wu
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, General Classification, Position, Sentiment Analysis

This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}. The goal of this task is to classify the emotions of excluded words in tweets into six different classes: sad, joy, disgust, surprise, anger and fear. For this, we examine a BiLSTM architecture with attention mechanism (BiLSTM-Attention) and a LSTM architecture with attention mechanism (LSTM-Attention), and try different dropout rates based on these two models. We then exploit an ensemble of these methods to give the final prediction which improves the model performance significantly compared with the baseline model. The proposed method achieves 7th position out of 30 teams and outperforms the baseline method by 12.5{\%} in terms of \textit{macro F1}.
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification
Authors: Flor Miriam Plaza-del-Arco, Eugenio Mart{\'\i}nez-C{\'a}mara, Maite Martin, L. Alfonso Ure{\~n}a- L{\'o}pez
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, Emotion Recognition, General Classification, Sentiment Analysis

In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets
Authors: Man Liu
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: regression, Word Embeddings

This paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three different approaches: feed forward neural network (FFNN), convolutional BLSTM (ConBLSTM) and Gradient Boosting Regression Tree Method (GBM). Word embeddings used in convolutional BLSTM are pre-trained on 470 million tweets which are filtered using the emotional words and emojis. In addition, broad sets of features (i.e. syntactic features, lexicon features, cluster features) are adopted to train GBM and FFNN. The three approaches are finally ensembled by the weighted average of predicted probabilities of each emotion label.
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets
Authors: Wenting Wang
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Common Sense Reasoning, Emotion Classification, Emotion Recognition, General Classification, Word Embeddings

This paper describes our system designed for the WASSA-2018 Implicit Emotion Shared Task (IEST). The task is to predict the emotion category expressed in a tweet by removing the terms \textit{angry}, \textit{afraid}, \textit{happy}, \textit{sad}, \textit{surprised}, \textit{disgusted} and their synonyms. Our final submission is an ensemble of one supervised learning model and three deep neural network based models, where each model approaches the problem from essentially different directions. Our system achieves the macro F1 score of 65.8{\%}, which is a 5.9{\%} performance improvement over the baseline and is ranked 12 out of 30 participating teams.
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning
Authors: Yasas Senarath, Uthayasanker Thayasivam
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, Emotion Classification, General Classification, Opinion Mining, Sentiment Analysis, Text Classification

This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8{\%} without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASSA-2018.