Mark Kröll

Movement prediction from real-world images using a liquid state machine

Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor readings from an environment can be processed, a controller can be stabilized and thus the performance of a moving robot in a real-world environment is improved. So far, only experiments with artificially generated data have shown good results. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. This pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. Our results support the idea that learning with a liquid state machine can be applied not only to designed data but also to real, noisy data.

Towards linking buyers and sellers: detecting commercial Intent on twitter

Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57% and recall 77% scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.

Analyzing human intentions in natural language text

In this paper, we introduce the idea of Intent Analysis, which is to create a profile of the goals and intentions present in textual content. Intent Analysis, similar to Sentiment Analysis, represents a type of document classification that differs from traditional topic categorization by focusing on classification by intent. We investigate the extent to which the automatic analysis of human intentions in text is feasible and report our preliminary results, and discuss potential applications. In addition, we present results from a study that focused on evaluating intent profiles generated from transcripts of American presidential candidate speeches in 2008.

Analysis of machine learning techniques for context extraction

dasiaContext is keypsila conveys the importance of capturing the digital environment of a knowledge worker. Knowing the userpsilas context offers various possibilities for support, like for example enhancing information delivery or providing work guidance. Hence, user interactions have to be aggregated and mapped to predefined task categories. Without machine learning tools, such an assignment has to be done manually. The identification of suitable machine learning algorithms is necessary in order to ensure accurate and timely classification of the userpsilas context without inducing additional workload. This paper provides a methodology for recording user interactions and an analysis of supervised classification models, feature types and feature selection for automatically detecting the current task and context of a user. Our analysis is based on a real world data set and shows the applicability of machine learning techniques.

Acquiring knowledge about human goals from Search Query Logs

A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. For this purpose, we devise an algorithm that automatically identifies queries containing explicit goals such as find home to rent in Florida. Evaluation results of our algorithm achieve useful precision/recall values. We apply the classification algorithm to two large Search Query Logs, recorded by AOL and Microsoft Research in 2006, and obtain a set of ~110,000 queries containing explicit goals. To study the nature of human goals in Search Query Logs, we conduct qualitative, quantitative and comparative analyses. Our findings suggest that Search Query Logs (i) represent a viable source for extracting human goals, (ii) contain a great variety of human goals and (iii) contain human goals that can be employed to complement existing commonsense knowledge bases. Finally, we illustrate the potential of goal knowledge for addressing following application scenario: to refine and extend commonsense knowledge with human goals from Search Query Logs. This work is relevant for (i) knowledge engineers interested in acquiring human goals from textual corpora and constructing knowledge bases of human goals (ii) researchers interested in studying characteristics of human goals in Search Query Logs.

Synergizing standard and ad-hoc processes

In a knowledge-intensive business environment, knowledge workers perform their tasks in highly creative ways. This essential freedom required by knowledge workers often conflicts with their organizations need for standardization, control, and transparency. Within this context, the research project DYONIPOS aims to mitigate this contradiction by supporting the process engineer with insights into the process executers working behavior. These insights constitute the basis for balanced process modeling. DYONIPOS provides a process engineer support environment with advanced process modeling services, such as process visualization, standard process validation, and ad-hoc process analysis and optimization services.

Exploiting Propositions for Opinion Mining

With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to drive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.

Studying databases of intentions: do search query logs capture knowledge about common human goals?

Access to knowledge about common human goals has been found critical for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about common human goals represents a major challenge. In a departure from existing approaches, this paper investigates a novel resource for knowledge acquisition: The utilization of search query logs for this task. By relating goals contained in search query logs with goals contained in existing commonsense knowledge bases such as ConceptNet, we aim to shed light on the usefulness of search query logs for capturing knowledge about common human goals. The main contribution of this paper consists of an empirical study comparing common human goals contained in two large search query logs (AOL and Microsoft Research) with goals contained in the commonsense knowledge base ConceptNet. The paper sketches ways how goals from search query logs could be used to address the goal acquisition and goal coverage problem related to common-sense knowledge bases.

Dynamic topography information landscapes: an incremental approach to visual knowledge discovery

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.

Authorship identification of documents with high content similarity

AbstractThe goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.