Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.

When a website hosting user-generated content asks users a straightforward question - "Was this content helpful?" with one "Yes" and one "No" button as the two possible answers - one might expect to get a straightforward answer. In this paper, we explore how users respond to this question and find that their responses are not quite straightforward after all. Using data from Amazon product reviews, we present evidence that users do not make absolute, independent voting decisions based on individual review quality alone. Rather, whether users vote at all, as well as the polarity of their vote for any given review, depends on the context in which they view it - reviews receive a larger overall number of votes when they are 'misranked', and the polarity of votes becomes more positive/negative when the review is ranked lower/higher than it deserves. We distill these empirical findings into a new probabilistic model of rating behavior that includes the dependence of rating decisions on context. Understanding and formally modeling voting behavior is crucial for designing learning mechanisms and algorithms for review ranking, and we conjecture that many of our findings also apply to user behavior in other online content-rating settings.

To facilitate direct comparisons between different products, we present an approach to constructing short and comparative summaries based on product reviews. In particular, the user can view automatically aligned pairs of snippets describing reviewers' opinions on different features (also selected automatically by our approach) for two selected products. We propose a submodular objective function that avoids redundancy, that is efficient to optimize, and that aligns the snippets into pairs. Snippets are chosen from product reviews and thus easy to obtain. In our experiments, we show that the method constructs qualitatively good summaries, and that it can be tuned via supervised learning.

In many areas of life, we now have almost complete electronic archives reaching back for well over two decades. This includes, for example, the body of research papers in computer science, all news articles written in the US, and most people's personal email. However, we have only rather limited methods for analyzing and understanding these collections. While keyword-based retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a temporal summary of such corpora in terms of landmark documents, authors, and topics. In particular, we explicitly model the temporal nature of influence between documents and re-interpret summarization as a coverage problem over words anchored in time. The resulting models provide monotone sub-modular objectives for computing informative and non-redundant summaries over time, which can be efficiently optimized with greedy algorithms. Our empirical study shows the effectiveness of our approach over several baselines.

In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multidocument summarization. By taking a structured prediction approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. The learning method applies to all submodular summarization methods, and we demonstrate its effectiveness for both pairwise as well as coverage-based scoring functions on multiple datasets. Compared to state-of-the-art functions that were tuned manually, our method significantly improves performance and enables high-fidelity models with number of parameters well beyond what could reasonably be tuned by hand.

In this paper, we present a document visualization technique for data analysis based on the semantic representation of text in the form of a directed graph, referred to as semantic graph. It is derived using natural language processing as follows. Firstly subject - verb - object triplets are automatically extracted from the Penn Treebank parse tree obtained for each sentence in the document. Secondly, the triplets are further enhanced by linking them to their corresponding co-referenced named entity, by resolving pronominal anaphors as well as attaching the associated WordNet synset. Starting from the document's semantic graph and the list of extracted triplets we automatically generate the document summary, for which we also derive the semantic representation.

HistoryViz provides a new perspective on a certain kind of textual data, in particular the data available in the Wikipedia, where different entities are described and put in historical perspective. Instead of browsing through pages each describing a certain topic, we can look at the relations between entities and events connected with the selected entities. The presented solution implemented in HistoryViz provides user with a graphical interface allowing viewing events concerning the selected person on a timeline and viewing relations to other entities as a graph that can be dynamically expanded.

In this paper, we present a technique for visual analysis of documents based on the semantic representation of text in the form of a directed graph, referred to as semantic graph. This approach can aid data mining tasks, such as exploratory data analysis, data description and summarization. In order to derive the semantic graph, we take advantage of natural language processing, and carry out a series of operations comprising a pipeline, as follows. Firstly, named entities are identified and co-reference resolution is performed; moreover, pronominal anaphors are resolved for a subset of pronouns. Secondly, subject -- predicate -- object triplets are automatically extracted from the Penn Treebank parse tree obtained for each sentence in the document. The triplets are further enhanced by linking them to their corresponding co-referenced named entity, as well as attaching the associated WordNet synset, where available. Thus we obtain a semantic directed graph composed of connected triplets. The document's semantic graph is a starting point for automatically generating the document summary. The model for summary generation is obtained by machine learning, where the features are extracted from the semantic graph structure and content. The summary also has an associated semantic representation. The size of the semantic graph, as well as the summary length can be manually adjusted for an enhanced visual analysis. We also show how to employ the proposed technique for the Visual Analytics challenge.

In this paper, we present an approach providing generalized relations for automatic ontology building based on frequent word n-grams. Using publicly available Google n-grams as our data source we can extract relations in form of triples and compute generalized and more abstract models. We propose an algorithm for building abstractions of the extracted triples using WordNet as background knowledge. We also present a novel approach to triple extraction using heuristics, which achieves notably better results than deep parsing applied on n-grams. This allows us to represent information gathered from the web as a set of triples modeling the common and frequent relations expressed in natural language. Our results have potential for usage in different settings including providing for a knowledge base for reasoning or simply as statistical data useful in improving understanding of natural languages.