In Proceedings of the IEEE Workshop on Exploring Opportunities and Challenges for Natural Language Techniques to Support Visual Analysis.

Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate the subtleties of topic transitions. Thus, this study annotates 215 conversations from the switchboard corpus and investigates how variables such as length, number of topic transitions, topic transitions share by participants and turns/topic are related. This work presents an empirical study on topic transition in switchboard corpus followed by modelling topic transition with a precision of 83% for in-domain(id) test set and 82% on 10 out-of-domain}(ood) test set. It is envisioned that this work will help in emulating human-human like topic transition in open-domain dialog systems.

Responding to the need for semantic lexical resources in natural language processing applications, we examine methods to acquire noun compounds (NCs), e.g., "orange juice", together with suitable fine-grained semantic interpretations, e.g., "squeezed from", which are directly usable as paraphrases. We employ bootstrapping and web statistics, and utilize the relationship between NCs and paraphrasing patterns to jointly extract NCs and such patterns in multiple alternating iterations. In evaluation, we found that having one compound noun fixed yields both a higher number of semantically interpreted NCs and improved accuracy due to stronger semantic restrictions.

An important challenge for the automatic analysis of English written text is the abundance of noun compounds: sequences of nouns acting as a single noun. In our view, their semantics is best characterized by the set of all possible paraphrasing verbs, with associated weights, e.g., malaria mosquito is carry (23), spread (16), cause (12), transmit (9), etc. Using Amazon's Mechanical Turk, we collect paraphrasing verbs for 250 noun-noun compounds previously proposed in the linguistic literature, thus creating a valuable resource for noun compound interpretation. Using these verbs, we further construct a dataset of pairs of sentences representing a special kind of textual entailment task, where a binary decision is to be made about whether an expression involving a verb and two nouns can be transformed into a noun compound, while preserving the sentence meaning.

In 1818, Mary Shelley created popular culture's first and most enduring monster in "Frankenstein; or, The Modern Prometheus." Since then, "women have always been the most important part of monster movies," as Mallory O'Meara states in "The Lady From the Black Lagoon," her engaging and compelling, if uneven, book about artist Milicent Patrick, the unsung designer of another iconic monster. As a teenager, indie horror filmmaker O'Meara became captivated by Universal Pictures' 1954 "Creature From the Black Lagoon." Its eponymous amphibian star -- a scaled, humanoid figure fondly known to generations of sci-fi geeks as the Gill-man -- was the last of Universal's classic monsters, joining the studio's pantheon alongside Dracula, the Frankenstein monster and his Bride, and the Wolfman, among others. The Gill-man was also, as O'Meara learned to her delighted amazement, the first -- and at the time, only -- movie monster to have been designed by a woman.

This helps them quickly test the best way to share their content across different platforms. Reddit recently launched a similar "tl;dr bot" that summarizes long posts into digestible snippets.

Want to know what it's like to interview Jennifer Lawrence? Google Home can tell you. Google and Vogue inked a partnership that brings the magazine's September issue to the voice-activated Home devices by prompting consumers to ask a handful of questions about the issue's celebrity coverage, hinting that Google Home is starting to open up to marketers and publishers. Vogue and Google Home have created voice content with the magazine's writers about the stories they wrote for the issue. For example, saying, "OK Google, ask Vogue to tell me more about Jennifer Lawrence," prompts Jason Gay, who wrote Vogue's cover story about Lawrence, to talk about interviewing the actress.

Finance leaders are recognizing that their current ERP systems, often heavily customized and woefully behind on updates, are becoming an albatross--and they're moving quickly to explore new applications. An Accenture survey of some 250 finance executives found that their organizations have adopted blockchain, in-memory computing, artificial intelligence, and robotic process automation at nearly the same rates as mobile, data analytics, and cloud technologies. But implementing such modern technologies while clinging to old processes and organizational structures built on old ERP platforms is like replacing the engine in a Tesla with one from my first car (a 1972 Ford Pinto). You'll end up stranded on the side of the road while others speed by your expensive heap. "If you automate an inefficient process, you just get bad data faster," said David Axson, managing director of Accenture, during a session at Oracle's Modern Finance Experience conference in Boston recently.

The "Talking Dead" host and the publishing heiress exchanged "I dos" on Saturday, Aug. 20, at The Langham Huntington, a luxury resort hotel in Pasadena, Calif., according to Entertainment Tonight. Hardwick, 44, donned a custom Prada tuxedo during the wedding ceremony, which was attended by about 450 guests. The 31-year-old bride, on the other hand, wore a custom-made Christian Siriano gown, which she matched with a pair of pavé crystal Stuart Weitzman shoes. Since Hardwick and Hearst share a love for science fiction, their wedding florist Mark Held drew inspiration from the couple's favorite sci-fi and comic book series. People reported that Held put red roses, Tardis blue hydrangea and Vanda orchids around the venue to give the nuptials a very unique touch.

Semantic taxonomies such as WordNet provide a rich source of knowledge for natural language processing applications, but are expensive to build, maintain, and extend. Motivated by the problem of automatically constructing and extending such taxonomies, in this paper we present a new algorithm for automatically learning hypernym (isa) relations from text. Our method generalizes earlier work that had relied on using small numbers of handcrafted regular expression patterns to identify hypernym pairs. Using "dependency path" features extracted from parse trees, we introduce a general-purpose formalization and generalization of these patterns. Given a training set of text containing known hypernym pairs, our algorithm automatically extracts useful dependency paths and applies them to new corpora to identify novel pairs. On our evaluation task (determining whether two nouns in a news article participate in a hypernym relationship), our automatically extracted database of hypernyms attains both higher precision and higher recall than WordNet.