Recent progress in Deep Reinforcement Learning has shown that agents can be taught complex behaviour and solve difficult tasks, such as playing video games from pixel observations, or mastering the game of Go without observing human games, with ...

Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels ...

Recent knowledge extraction methods are moving towards ternary and higher-arity relations to capture more information about binary facts. An example is to include the time, the location, and the duration of a specific fact. These relations can be even ...

A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, existing deep models are mainly designed for the semantic matching between ...

This paper addresses the problem ofmulti-instance entity typing from corpus. Current approaches mainly rely on the structured features (\textitattributes, attribute-value pairs andtags ) of the entities. However, their effectiveness is largely dependent ...

In this paper, we advance the state-of-the-art in topic modeling by means of the design and development of a novel (semi-formal) general topic modeling framework. The novel contributions of our solution include: (i) the introduction of new semantically-...

Linked Open Data (LOD) and social media often contain the representations of the same real-world entities, such as persons and organizations. These representations are increasingly interlinked, making it possible to combine and leverage both LOD and ...

Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through ...

Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and ...

In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly ...

In this paper, we introduce and tackle the Question Headline Generation (QHG) task. The motivation comes from the investigation of a real-world news portal where we find that news articles with question headlines often receive much higher click-through ...

Supervised learning methods are widely used in sentiment classification. However, when sentiment distribution is imbalanced, the performance of these methods declines. In this paper, we propose an effective approach for imbalanced sentiment ...

With the increasing uptake of knowledge graphs comes an increasing need for validating the knowledge contained in these graphs. However, the sheer size and number of knowledge bases used in real-world applications makes manual fact checking impractical. ...

Science journalism is the art of conveying a detailed scientific research paper in a form that non-scientists can understand and appreciate while ensuring that its underlying information is conveyed accurately. It plays a crucial role in making ...

One of the challenges of automating machine learning applications is the automatic selection of an algorithmic model for a given problem. We present AutoDi, a novel and resource-efficient approach for model selection. Our approach combines two sources ...

Wireless coverage is the received signal strength of a particular region, which is a key prerequisite to provide high quality mobile communication service. In this paper, we aim to estimate the wireless coverage of an area based on the randomly ...

It has been shown that stock price movements are influenced by news. To predict stock movements with news, many existing works rely only on the news title since the news content may contain irrelevancies which seriously degrade the prediction accuracy. ...

Sentiment analysis and opinion mining are significant and valuable for subject information extraction from the text. Word embedding that can map the words to low-dimensional vector representations has been widely used in natural language processing ...

In machine reading comprehension (MRC) tasks, sentence inference is an important but extremely difficult problem. Most of MRC models directly interact articles with questions from the word level, which ignores inter and intra information of sentences ...

This study takes the lead to study the aspect/sentiment-aware abstractive review summarization in domain adaptation scenario. The proposed model CASAS (neural attentive model for Cross-domain Aspect/Sentiment-aware Abstractive review Summarization) ...