Modern Graph Theory

Conversational interfaces recently gained a lot of attention One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and so-called Natural Language Understanding (NLU) services While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically In this paper, we present a method to evaluate the classification performance of NLU services Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers Based on these corpora, we conduct an evaluation of some of the most popular NLU services Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use

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Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

The concept of event processing is established as a generic computational paradigm in various application fields. Events report on state changes of a system and its environment. Complex event recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings. We also highlight future research directions.

Complex event recognition in the Big Data era: a survey

Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for event stream processing technology to analyze this vast amount of sequential data to enable online operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. We propose a novel E-Cube model which combines CEP and OLAP techniques for efficient multi-dimensional event pattern analysis at different abstraction levels. Our analysis of the interrelationships in both concept abstraction and pattern refinement among queries facilitates the composition of these queries into an integrated E-Cube hierarchy. Based on this E-Cube hierarchy, strategies of drill-down (refinement from abstract to more specific patterns) and of roll-up (generalization from specific to more abstract patterns) are developed for the efficient workload evaluation. Our proposed execution strategies reuse intermediate results along both the concept and the pattern refinement relationships between queries. Based on this foundation, we design a cost-driven adaptive optimizer called Chase, that exploits the above reuse strategies for optimal E-Cube hierarchy execution. Our experimental studies comparing alternate strategies on a real world financial data stream under different workload conditions demonstrate the superiority of the Chase method. In particular, our Chase execution in many cases performs ten fold faster than the state-of-the art strategy for real stock market query workloads.

E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing

A framework for real-time semantic social media analysis

Many modern applications including tag based mass transit systems, RFID-based supply chain management systems and online financial feeds require special purpose event stream processing technology to analyze vast amounts of sequential multi-dimensional data available in real-time data feeds. Traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while Complex Event Processing (CEP) systems are designed for sequence detection and do not support OLAP operations. We will demonstrate a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. A London transit scenario will be given to demonstrate the utility and performance of this proposed technology.

E-Cube: Multi-dimensional event sequence processing using concept and pattern hierarchies

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.

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A Reverse Approach to Named Entity Extraction and Linking in Microposts.

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly important genre. This paper presents the MIT Information Extraction Toolkit (MITIE) and explores its adaptability to the micropost genre. CCS Concepts •Information systems ➝ Information extraction

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Named Entity Recognition in 140 Characters or Less.

Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using context and content cues. We construct a rich graph structure induced by hashtags and social communications in Twitter. We derive features from this graph structure—centrality, communities, and local flow of information. In addition, we perform detailed content analysis on tweets looking at offensiveness and topics. We then examine user classification and the role of feature types (context, content) and learning methods (propositional, relational) through a series of experiments on annotated data. Our work contrasts with prior approaches in that we use relational learning and alternative, non-specialized feature sets. Our goal is to understand how both content and context are predictive of user characteristics. Experiments demonstrate that the best performance for user classification uses relational learning with varying content and context features.

Kara Greenfield

Papers

E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing

E-Cube: Multi-dimensional event sequence processing using concept and pattern hierarchies

A Reverse Approach to Named Entity Extraction and Linking in Microposts.

Named Entity Recognition in 140 Characters or Less.

Content+Context=Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification