The semantic information of documents needs to be represented because it is the basis for many applications, such as document summarization, web search, and text analysis. Although many studies have explored this problem by enriching document vectors with the relatedness of the words involved, the performance remains far from satisfactory because the physical boundaries of documents hinder the evaluation of the relatedness between words. To address this problem, we propose an effective approach to further infer the implicit relatedness between words via their common related words. To avoid overestimation of the implicit relatedness, we restrict the inference in terms of the marginal probabilities of the words based on the law of total probability. The proposed method measures the relatedness between words, which is confirmed theoretically and experimentally. Thorough evaluation on real datasets illustrates that significant improvement on document clustering has been achieved with the proposed method compared with state-of-the-art methods.

Comparable corpora serve as an important substitute for parallel resources in cases of under-resourced language pairs. Previous work mostly aims to find a better strategy to exploit existing comparable corpora, while ignoring the variety in corpus quality. The quality of comparable corpora affects a lot its usability in practice, a fact that has been justified by several studies. However, researchers have not been able to establish a widely accepted and fully validated framework to measure corpus quality. We will thus investigate in this paper a comprehensive methodology to deal with the quality of comparable corpora. To be exact, we will propose several comparability measures and a quantitative strategy to test those measures. Our experiments show that the proposed comparability measure can capture gold-standard comparability levels very well and is robust to the bilingual dictionary used. Moreover, we will show in the task of bilingual lexicon extraction that the proposed measure correlates well with the performance of the real world application.

Despite the long history of Natural Language Generation (NLG) research, the potential for influencing real world behaviour through automatically generated texts has not received much attention. In this paper, we present SaferDrive, a behaviour change support system that uses NLG and telematic data in order to create weekly textual feedback for automobile drivers, which is delivered through a smartphone application. Usage-based car insurances use sensors to track driver behaviour. Although the data collected by such insurances could provide detailed feedback about the driving style, they are typically withheld from the driver and used only to calculate insurance premiums. SaferDrive instead provides detailed textual feedback about the driving style, with the intent to help drivers improve their driving habits. We evaluate the system with real drivers and report that the textual feedback generated by our system does have a positive influence on driving habits, especially with regard to speeding.

Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one’s most intimately held thoughts, opinions, and beliefs. This relatively new type of data is able to represent finer and more narrowly defined demographic slices. If until now researchers have primarily focused on leveraging personalized content to identify latent information such as gender, nationality, location, or age, this article seeks to establish a structured way of extracting possessions, or items that people own or are entitled to, as a way to ultimately provide insights into people’s behaviors and characteristics. We introduce the new task of ‘possession identification in text’, and release a novel dataset where possessions are marked at different confidence levels. We present experiments and results obtained when seeking to automatically identify and extract possessions from the text.

In this paper, we study the problem of aspect-based sentiment analysis. Our model simultaneously extracts aspect-specific opinion expressions and determines the rating for each aspect in reviews. Previous works have mainly focused on the problem of opinion phrase extraction and aspect rating prediction in a pipelined manner and are not able to capture the dependencies of aspect opinion expression on aspect rating and vice-versa. They are also unable to discover aspect-specific opinion expressions and their associated rating scores. We present a joint modelling approach to extract aspect-specific sentiment expression and aspect rating prediction simultaneously. This paper proposes a novel LDA–CRF hybrid model which employs discriminative conditional random field component for phrase extraction, a regression component for rating prediction and a generative component for grouping aspect–sentiment expressions (aspect-specific opinion expressions) into coherent topics. To show the effectiveness of our approach, we evaluate the performance of the model on both task: (i) aspect-specific opinion expressions and (ii) rating prediction on the dataset of hotel and restaurant reviews from TripAdvisor.com. Experimental results show that both task potentially reinforce each other and joint modeling outperformed state-of-the-art baselines for each individual tasks.

It is amazing that I have written for more than a year on Emerging Trends without mentioning China’s investments in Artificial Intelligence (AI). Now that I have moved to Baidu, I would like to take this opportunity to share some of my personal observations with what’s happening in China over the past 25 years. The top universities in China have always been very good, but they are better today than they were 25 years ago, and they are on a trajectory to become the biggest and the best in the world. China is investing big time in what we do, both in the private sector and the public sector. Kai-Fu Lee is bullish on his investments in AI and China. There is a bold government plan for AI with specific milestones for parity with the West in 2020, major breakthroughs by 2025 and the envy of the world by 2030.