"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.Comment: ACL 201

Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification

Representing a valuable human-computer interaction interface, Sentiment Analysis (SA) is applied to a wide range of problems. In the present paper, the researchers introduce a novel concept of Business Sentiment (BS) as a measurement of a Perceived Anticipated Effort (PAE) in the context of business processes (BPs). BS is considered as an emotional component of BP task contextual complexity perceived by a process worker after reading the task text. PAE is interpreted as a business process (BP) key performance indicator predicting urgency, criticality and complexity of the BP task processing. Using qualitative evaluation, the researchers proved the workability of both BS concept and its effective application method to measure PAE. As practical contributions of the research, quantitative support in a form of statistical reports and qualitative support in a form of task prioritization recommendations and time management for a BP worker are suggested

Domain adaptation in Natural Language Processing

Domain adaptation has received much attention in the past decade. It has been shown that domain knowledge is paramount for building successful Natural Language Processing (NLP) applications. To investigate the domain adaptation problem, we conduct several experiments from different perspectives. First, we automatically adapt sentiment dictionaries for predicting the financial outcomes “excess return” and “volatility”. In these experiments, we compare manual adaptation of the domain-general dictionary with automatic adaptation, and manual adaptation with a combination consisting of first manual, then automatic adaptation. We demonstrate that automatic adaptation performs better than manual adaptation, namely the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting excess return and volatility. Furthermore, we perform qualitative and quantitative analyses finding that annotation based on an expert’s a priori belief about a word’s meaning is error-prone – the meaning of a word can only be recognized in the context that it appears in. Second, we develop the temporal transfer learning approach to account for the language change in social media. The language of social media is changing rapidly – new words appear in the vocabulary, and new trends are constantly emerging. Temporal transfer-learning allows us to model these temporal dynamics in the document collection. We show that this method significantly improves the prediction of movie sales from discussions on social media forums. In particular, we illustrate the success of parameter transfer, the importance of textual information for financial prediction, and show that temporal transfer learning can capture temporal trends in the data by focusing on those features that are relevant in a particular time step, i.e., we obtain more robust models preventing overfitting. Third, we compare the performance of various domain adaptation models in low-resource settings, i.e., when there is a lack of large amounts of high-quality training data. This is an important issue in computational linguistics since the success of NLP applications primarily depends on the availability of training data. In real-world scenarios, the data is often too restricted and specialized. In our experiments, we evaluate different domain adaptation methods under these assumptions and find the most appropriate techniques for such a low-data problem. Furthermore, we discuss the conditions under which one approach substantially outperforms the other. Finally, we summarize our work on domain adaptation in NLP and discuss possible future work topics.Die Domänenanpassung hat in den letzten zehn Jahren viel Aufmerksamkeit erhalten. Es hat sich gezeigt, dass das Domänenwissen für die Erstellung erfolgreicher NLP-Anwendungen (Natural Language Processing) von größter Bedeutung ist. Um das Problem der Domänenanpassung zu untersuchen, führen wir mehrere Experimente aus verschiedenen Perspektiven durch. Erstens passen wir Sentimentlexika automatisch an, um die Überschussrendite und die Volatilität der Finanzergebnisse besser vorherzusagen. In diesen Experimenten vergleichen wir die manuelle Anpassung des allgemeinen Lexikons mit der automatischen Anpassung und die manuelle Anpassung mit einer Kombination aus erst manueller und dann automatischer Anpassung. Wir zeigen, dass die automatische Anpassung eine bessere Leistung erbringt als die manuelle Anpassung: das automatisch angepasste Sentimentlexikon übertrifft den bisherigen Stand der Technik bei der Vorhersage der Überschussrendite und der Volatilität. Darüber hinaus führen wir eine qualitative und quantitative Analyse durch und stellen fest, dass Annotationen, die auf der a priori Überzeugung eines Experten über die Bedeutung eines Wortes basieren, fehlerhaft sein können. Die Bedeutung eines Wortes kann nur in dem Kontext erkannt werden, in dem es erscheint. Zweitens entwickeln wir den Ansatz, den wir Temporal Transfer Learning benennen, um den Sprachwechsel in sozialen Medien zu berücksichtigen. Die Sprache der sozialen Medien ändert sich rasant – neue Wörter erscheinen im Vokabular und es entstehen ständig neue Trends. Temporal Transfer Learning ermöglicht es, diese zeitliche Dynamik in der Dokumentensammlung zu modellieren. Wir zeigen, dass diese Methode die Vorhersage von Filmverkäufen aus Diskussionen in Social-Media-Foren erheblich verbessert. In unseren Experimenten zeigen wir (i) den Erfolg der Parameterübertragung, (ii) die Bedeutung von Textinformationen für die finanzielle Vorhersage und (iii) dass Temporal Transfer Learning zeitliche Trends in den Daten erfassen kann, indem es sich auf die Merkmale konzentriert, die in einem bestimmten Zeitschritt relevant sind, d. h. wir erhalten robustere Modelle, die eine Überanpassung verhindern. Drittens vergleichen wir die Leistung verschiedener Domänenanpassungsmodelle in ressourcenarmen Umgebungen, d. h. wenn große Mengen an hochwertigen Trainingsdaten fehlen. Das ist ein wichtiges Thema in der Computerlinguistik, da der Erfolg der NLP-Anwendungen stark von der Verfügbarkeit von Trainingsdaten abhängt. In realen Szenarien sind die Daten oft zu eingeschränkt und spezialisiert. In unseren Experimenten evaluieren wir verschiedene Domänenanpassungsmethoden unter diesen Annahmen und finden die am besten geeigneten Techniken dafür. Darüber hinaus diskutieren wir die Bedingungen, unter denen ein Ansatz den anderen deutlich übertrifft. Abschließend fassen wir unsere Arbeit zur Domänenanpassung in NLP zusammen und diskutieren mögliche zukünftige Arbeitsthemen

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc