Neural approaches to sequence labeling for information extraction

Een belangrijk aspect binnen artificiële intelligentie (AI) is het interpreteren van menselijke taal uitgedrukt in tekstuele (geschreven) vorm: natural Language processing (NLP) is belangrijk gezien tekstuele informatie nuttig is voor veel toepassingen. Toch is het verstaan ervan (zogenaamde natural Language understanding, (NLU) een uitdaging, gezien de ongestructureerde vorm van tekst, waarvan de betekenis vaak dubbelzinnig en contextafhankelijk is. In dit proefschrift introduceren we oplossingen voor tekortkomingen van gerelateerd werk bij het behandelen van fundamentele taken in natuurlijke taalverwerking, zoals named entity recognition (i.e. het identificeren van de entiteiten die in een zin voorkomen) en relatie-extractie (het identificeren van relaties tussen entiteiten). Vertrekkend van een specifiek probleem (met name het identificeren van de structuur van een huis aan de hand van een tekstueel zoekertje), bouwen we stapsgewijs een complete (geautomatiseerde) oplossing voor de bovengenoemde taken, op basis van neutrale netwerkarchitecturen. Onze oplossingen zijn algemeen toepasbaar op verschillende toepassingsdomeinen en talen. We beschouwen daarnaast ook de taak van het identificeren van relevante gebeurtenissen tijdens een evenement (bv. een doelpunt tijdens een voetbalwedstrijd), in informatiestromen op Twitter. Meer bepaald formuleren we dit probleem als het labelen van woord sequenties (vergelijkbaar met named entity recognition), waarbij we de chronologische relatie tussen opeenvolgende tweets benutten

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

What Am I Reading?: Article-style Native Advertisements in Canadian Newspapers

Native ads are ubiquitous in the North American digital news context. Their form, content and presentational style are practically indistinguishable from regular news editorials, and thus are often mistaken for informative content by newsreaders. This advertising practice is deceptive, in that it exploits loopholes in human digital literacy. Despite this, it is flourishing as a lucrative digital news advertising format. This paper documents and compares the 2018 Canadian news editorial writing and advertising practices in an effort to highlight their similarities and differences for potential automatic detection and categorization. We collected 10 native ads and 10 editorial pieces from 4 Canadian newspapers. The 80 analyzed articles consisted of 40 native ads content-matched to editorials in the same newspaper. The individually-matched pairs and overall practices in the 2 groups were content-analyzed and compared. Native ads did not differ much from editorial articles in content but were likely to be surrounded by different types of advertising. In addition, advertisement labelling practices were inconsistent across national papers. We call for increased efforts in regulation and automatic detection of convert advertising by a more nuanced categorization and their more explicit labeling in the digital news

Analyzing web behavior in indoor retail spaces

We analyze 18- million rows of Wi-Fi access logs collected over a 1-year period from over 120,000 anonymized users at an inner city shopping mall. The anonymized data set gathered from an opt-in system provides users' approximate physical location as well as web browsing and some search history. Such data provide a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their web behavior, serving as a proxy to their information needs. We found that (a) there is a weekly periodicity in users' visits to the mall; (b) people tend to visit similar mall locations and web content during their repeated visits to the mall; (c) around 60% of registered Wi-Fi users actively browse the web, and around 10% of them use Wi-Fi for accessing web search engines; (d) people are likely to spend a relatively constant amount of time browsing the web while the duration of their visit may vary; (e) the physical spatial context has a small, but significant, influence on the web content that indoor users browse; and (f) accompanying users tend to access resources from the same web domains

February 14, 2005

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Montana Kaimin, October 23, 1980

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/8130/thumbnail.jp