Rexha Andi, Kern Roman, Dragoni Mauro , Kröll Mark
2016
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.
Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman
2016
To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of scientific publications. Presently, common bibliographic metrics often attribute the entire article to all the authors affecting author-specific retrieval processes. We envision a more finegrained analysis of scientific authorship by attributing particular segments to authors. To realize this vision, we propose a new feature representation of scientific publications that captures the distribution of tylometric features. In a classification setting, we then seek to predict the number of authors of a scientific article. We evaluate our approach on a data set of ~ 6100 PubMed articles and achieve best results by applying random forests, i.e., 0.76 precision and 0.76 recall averaged over all classes.
Rexha Andi, Kröll Mark, Kern Roman
2016
Monitoring (social) media represents one means for companies to gain access to knowledge about, for instance, competitors, products as well as markets. As a consequence, social media monitoring tools have been gaining attention to handle amounts of data nowadays generated in social media. These tools also include summarisation services. However, most summarisation algorithms tend to focus on (i) first and last sentences respectively or (ii) sentences containing keywords.In this work we approach the task of summarisation by extracting 4W (who, when, where, what) information from (social)media texts. Presenting 4W information allows for a more compact content representation than traditional summaries. Inaddition, we depart from mere named entity recognition (NER) techniques to answer these four question types by includingnon-rigid designators, i.e. expressions which do not refer to the same thing in all possible worlds such as “at the main square”or “leaders of political parties”. To do that, we employ dependency parsing to identify grammatical characteristics for each question type. Every sentence is then represented as a 4W block. We perform two different preliminary studies: selecting sentences that better summarise texts by achieving an F1-measure of 0.343, as well as a 4W block extraction for which we achieve F1-measures of 0.932; 0.900; 0.803; 0.861 for “who”, “when”, “where” and “what” category respectively. In a next step the 4W blocks are ranked by relevance. The top three ranked blocks, for example, then constitute a summary of the entire textual passage. The relevance metric can be customised to the user’s needs, for instance, ranked by up-to-dateness where the sentences’ tense is taken into account. In a user study we evaluate different ranking strategies including (i) up-todateness,(ii) text sentence rank, (iii) selecting the firsts and lasts sentences or (iv) coverage of named entities, i.e. based on the number of named entities in the sentence. Our 4W summarisation method presents a valuable addition to a company’s(social) media monitoring toolkit, thus supporting decision making processes.
Pimas Oliver, Rexha Andi, Kröll Mark, Kern Roman
2016
The PAN 2016 author profiling task is a supervised classification problemon cross-genre documents (tweets, blog and social media posts). Our systemmakes use of concreteness, sentiment and syntactic information present in thedocuments. We train a random forest model to identify gender and age of a document’sauthor. We report the evaluation results received by the shared task.
Gursch Heimo, Ziak Hermann, Kröll Mark, Kern Roman
2016
Modern knowledge workers need to interact with a large number of different knowledge sources with restricted or public access. Knowledge workers are thus burdened with the need to familiarise and query each source separately. The EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project aims at developing a recommender system providing relevant and novel content to its users. Based on the user’s work context, the EEXCESS system can either automatically recommend useful content, or support users by providing a single user interface for a variety of knowledge sources. In the design process of the EEXCESS system, recommendation quality, scalability and security where the three most important criteria. This paper investigates the scalability aspect achieved by federated design of the EEXCESS recommender system. This means that, content in different sources is not replicated but its management is done in each source individually. Recommendations are generated based on the context describing the knowledge worker’s information need. Each source offers result candidates which are merged and re-ranked into a single result list. This merging is done in a vector representation space to achieve high recommendation quality. To ensure security, user credentials can be set individually by each user for each source. Hence, access to the sources can be granted and revoked for each user and source individually. The scalable architecture of the EEXCESS system handles up to 100 requests querying up to 10 sources in parallel without notable performance deterioration. The re-ranking and merging of results have a smaller influence on the system's responsiveness than the average source response rates. The EEXCESS recommender system offers a common entry point for knowledge workers to a variety of different sources with only marginally lower response times as the individual sources on their own. Hence, familiarisation with individual sources and their query language is not necessary.
Rexha Andi, Dragoni Mauro, Kern Roman, Kröll Mark
2016
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
Yusuke Fukazawa, Kröll Mark, Strohmaier M., Ota Jun
2016
Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.
Dragoni Mauro, Rexha Andi, Kröll Mark, Kern Roman
2016
Twitter is one of the most popular micro-blogging serviceson the web. The service allows sharing, interaction and collaboration viashort, informal and often unstructured messages called tweets. Polarityclassification of tweets refers to the task of assigning a positive or a nega-tive sentiment to an entire tweet. Quite similar is predicting the polarityof a specific target phrase, for instance@Microsoftor#Linux,whichiscontained in the tweet.In this paper we present a Word2Vec approach to automatically pre-dict the polarity of a target phrase in a tweet. In our classification setting,we thus do not have any polarity information but use only semantic infor-mation provided by a Word2Vec model trained on Twitter messages. Toevaluate our feature representation approach, we apply well-establishedclassification algorithms such as the Support Vector Machine and NaiveBayes. For the evaluation we used theSemeval 2016 Task #4dataset.Our approach achieves F1-measures of up to∼90 % for the positive classand∼54 % for the negative class without using polarity informationabout single words.
Pimas Oliver, Klampfl Stefan, Kohl Thomas, Kern Roman, Kröll Mark
2016
Patents and patent applications are important parts of acompany’s intellectual property. Thus, companies put a lot of effort indesigning and maintaining an internal structure for organizing their ownpatent portfolios, but also in keeping track of competitor’s patent port-folios. Yet, official classification schemas offered by patent offices (i) areoften too coarse and (ii) are not mappable, for instance, to a company’sfunctions, applications, or divisions. In this work, we present a first steptowards generating tailored classification. To automate the generationprocess, we apply key term extraction and topic modelling algorithmsto 2.131 publications of German patent applications. To infer categories,we apply topic modelling to the patent collection. We evaluate the map-ping of the topics found via the Latent Dirichlet Allocation method tothe classes present in the patent collection as assigned by the domainexpert.
Steinbauer Florian, Kröll Mark
2016
Social media monitoring has become an important means for business analytics and trend detection, for instance, analyzing the senti-ment towards a certain product or decision. While a lot of work has beendedicated to analyze sentiment for English texts, much less effort hasbeen put into providing accurate sentiment classification for the Germanlanguage. In this paper, we analyze three established classifiers for theGerman language with respect to Facebook posts. We then present ourown hierarchical approach to classify sentiment and evaluate it using adata set of∼640 Facebook posts from corporate as well as governmentalFacebook pages. We compare our approach to three sentiment classifiersfor German, i.e. AlchemyAPI, Semantria and SentiStrength. With anaccuracy of 70 %, our approach performs better than the other classi-fiers. In an application scenario, we demonstrate our classifier’s abilityto monitor changes in sentiment with respect to the refugee crisis.