Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

The Fourth International Conference on Knowledge Capture was held October 28-31, 2007, in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice-taking systems. This was the fourth in a series of meetings; the first was held in Victoria, British Columbia, in 2001; the second was collocated with the ISWC meeting and was held on Sanibel Island, Florida, in October 2003; and the third meeting was held in Banff, Alberta, in October 2005. The conference was held at the Fairmont Chateau in Whistler.

In preparation for the Summer Olympics in Barcelona, Telefonica de Espana S.A. (Madrid, Spain), the Spanish telephone company, has developed a Spanish-language speech recognition system. A caller will be able to access business information by dialing a toll-free number and saying "uno, " "dos" or "tres" into the phone in response to a recorded prompt (such as "say one for sales, two for service," etc.). An intelligent network system developed by ATT Spain will recognize the caller's speech input and send the data to the network for connection to the desired service. Varian Associates (Palo Alto, CA) has implemented an expert troubleshooting system to provide better service for its customers worldwide. The company's customer support personnel are now using a computer-aided intelligent service system to analyze equipment failures and track parts usage more rapidly.

It's 2017 so of course the phrase, "a robot cut through a wall to pass the Olympic torch on to its creator" is a real sentence to describe a real thing that actually happened. On Monday, "Hubo," a robot created at the Korean Advanced Institute of Science and Technology, passed the Olympic flame through a hole it cut in a wall to its creator, Professor Oh Jun-Ho. The passing of the torch was a part of the 101-day Olympic torch relay as the 2018 Winter Olympics fast approaches. The hole-cutting party trick was the same task Hubo performed in 2015 to win the DARPA Robotics Challenge. So a congratulations is in order to "Hubo" for carrying the Olympic flame and also to humanity for having handed fire to a robot and lived to tell the tale.

With everything from a mist curtain designed to cool people off in the oppressive summer heat to hydrogen fuel cell vehicles and robots delivering refreshing drinks, companies are sparing no expense as they gear up for a marketing bonanza. Panasonic hopes to see Hospi robots guiding hotel guests to their rooms and also providing room service. Toyota Motor Corp. has a blueprint for hydrogen fuel cell vehicles or buses, which only emit water, to ride around the Olympic venues in an official capacity. The automaker also hopes to promote the Toyota Mirai -- the world's first mass-produced hydrogen fuel cell vehicle that went on sale in December 2014 -- to Olympic officials and visitors.