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Gefühle beeinflussen das menschliche Verhalten, indem sie beispielsweise zu bestimmten Handlungen motivieren, vergangene Erlebnisse bewerten und die soziale Interaktion prägen. Auch bei der Aktivität der Internetsuche spielen Gefühle als subjektive Empfindungen eine wichtige Rolle, sodass sie im Fachgebiet Information Seeking Behavior erforscht werden. Die vorliegende Arbeit ist in der Disziplin der Informationswissenschaft verortet und zielt darauf ab, das Wissen über die Gefühle der Suchenden zu erweitern und daraus konstruktive Schlussfolgerungen zu ziehen. Sie geht der Frage nach, wie die Informationssuche im Internet emotional erlebt wird und welche Bedingungen und Ursachen die Suchenden als bedeutsam für ihr emotionales Erleben bei der Onlinesuche betrachten. Um dies zu erforschen, wird ein methodologischer Rahmen verwendet, der sich diesem Thema auf ganz andere Art annähert, als bisherige Forschungsarbeiten auf diesem Gebiet: Die Grounded Theory-Methodologie. Durch deren Prinzipien des Fragenstellens und Vergleichens entsteht eine Theorie, die gleichzeitig interpretierend als auch empirisch fundiert ist. Als Datengrundlage dieser Theorie dienen Leitfadeninterviews, in denen junge Erwachsene aus den USA und Deutschland ihre Eindrücke und Empfindungen bei der Internetsuche schildern. Die Teilnehmenden beziehen sich dabei auf eine unmittelbar vor dem Interview durchgeführte Internetsuche, in der sie durch ein eigenes Informationsbedürfnis angeleitet wurden. Als Ergebnis der Studie zeigt sich zum einen, wie stark die individuellen Suchthemen die Gefühle der Suchenden beeinflussen. Zum anderen ergibt die Untersuchung, dass diejenigen Gefühle, die sich auf die Ausführung der Suche beziehen, erstaunlich gering ausgeprägt sind, denn die Internetsuche wird als normale Routinehandlung empfunden. Aufgrund dieser Erkenntnisse zur Individualität und Alltäglichkeit der Sucherfahrung formuliert die vorliegende Arbeit Vorschläge für eine bessere Unterstützung der Suchenden und für die zukünftige Erforschung der affektiven Ebene bei der Onlinesuche.
We present a first attempt at classifying German tweets by region using only the text of the tweets. German Twitter users are largely unwilling to share geolocation data. Here, we introduce a two-step process. First, we identify regionally salient tweets by comparing them to an "average" German tweet based on lexical features. Then, regionally salient tweets are assigned to one of 7 dialectal regions. We achieve an accuracy (on regional tweets) of up to 50% on a balanced corpus, much improved from the baseline. Finally, we show several directions in which this work can be extended and improved.
In this paper, we describe our system developed for the GErman SenTiment AnaLysis shared Task (GESTALT) for participation in the Maintask 2: Subjective Phrase and Aspect Extraction from Product Reviews. We present a tool, which identifies subjective and aspect phrases in German product reviews. For the recognition of subjective phrases, we pursue a lexicon-based approach. For the extraction of aspect phrases from the reviews, we consider two possible ways: Besides the subjectivity and aspect look-up, we also implemented a method to establish which subjective phrase belongs to which aspect. The system achieves better results for the recognition of aspect phrases than for the subjective identification.
We report on the two systems we built for Task 1 of the German Sentiment Analysis Shared Task, the task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS). The first system is a rule-based system relying on a predicate lexicon specifying extraction rules for verbs, nouns and adjectives, while the second is a translation-based system that has been obtained with the help of the (English) MPQA corpus.
We present the German Sentiment Analysis Shared Task (GESTALT) which consists of two main tasks: Source, Subjective Expression and Target Extraction from Political Speeches (STEPS) and Subjective Phrase and Aspect Extraction from Product Reviews (StAR). Both tasks focused on fine-grained sentiment analysis, extracting aspects and targets with their associated subjective expressions in the German language. STEPS focused on political discussions from a corpus of speeches in the Swiss parliament. StAR fostered the analysis of product reviews as they are available from the website Amazon.de. Each shared task led to one participating submission, providing baselines for future editions of this task and highlighting specific challenges. The shared task homepage can be found at https://sites.google.com/site/iggsasharedtask/.
In this paper, we present our Named Entity Recognition (NER) system for German – NERU (Named Entity Rules), which heavily relies on handcrafted rules as well as information gained from a cascade of existing external NER tools. The system combines large gazetteer lists, information obtained by comparison of different automatic translations and POS taggers. With NERU, we were able to achieve a score of 73.26% on the development set provided by the GermEval 2014 Named Entity Recognition Shared Task for German.
This paper presents a Named Entity Recognition system for German based on Conditional Random Fields. The model also includes language-independant features and features computed form large coverage lexical resources. Along side the results themselves, we show that by adding linguistic resources to a probabilistic model, the results improve significantly.
In the latest decades, machine learning approaches have been intensively experimented for natural language processing. Most of the time, systems rely on using statistics within the system, by analyzing texts at the token level and, for labelling tasks, categorizing each among possible classes. One may notice that previous symbolic approaches (e.g. transducers) where designed to delimit pieces of text. Our research team developped mXS, a system that aims at combining both approaches. It locates boundaries of entities by using sequential pattern mining and machine learning. This system, intially developped for French, has been adapted to German.
In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples.
In this paper we present Nessy (Named Entity Searching System) and its application to German in the context of the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). We tackle the challenge by using a combination of machine learning (Naive Bayes classification) and rule-based methods. Altogether, Nessy achieves an F-score of 58.78% on the final test set.
This paper presents the BECREATIVE Named Entity Recognition system and its participation at the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). BECREATIVE uses a hybrid approach of two commonly used procedural methods, namely list-based lookups and machine learning (Naive Bayes Classification), which centers around the classifier. BECREATIVE currently reaches an F-score of 37.34 on the strict evaluation setting applied on the development set provided by GermEval.
This paper describes the DRIM Named Entity Recognizer (DRIM), developed for the GermEval 2014 Named Entity (NE) Recognition Shared Task. The shared task did not pose any restrictions regarding the type of named entity recognition (NER) system submissions and usage of external data, which still resulted in a very challenging task. We employ Linear Support Vector Classification (Linear SVC) in the implementation of SckiKit, with variety of features, gazetteers and further contextual information of the target words. As there is only one level of embedding in the dataset, two separate classifiers are trained for the outer and inner spans. The system was developed and tested on the dataset provided by the GermEval 2014 NER Shared Task. The overall strict (fine-grained) score is 70.94% on the development set, and 69.33% on the final test set which is quite promising for the German language.
This paper describes our classification and rule-based attempt at nested Named Entity Recognition for German. We explain how both approaches interact with each other and the resources we used to achieve our results. Finally, we evaluate the overall performance of our system which achieves an F-score of 52.65% on the development set and 52.11% on the final test set of the GermEval 2014 Shared Task.
MoSTNER is a German NER system based on machine learning with log-linear models and morphology-aware features. We use morphological analysis with Morphisto for generating features, moreover we use German Wikipedia as a gazetteer and perform punctuation-aware and morphology-aware page title matching. We use four types of factor graphs where NER labels are single variables or split into prefix (BILOU) and type (PER, LOC, etc.) variables. Our system supports nested NER (two levels), for training we use SampleRank, for prediction Iterated Conditional Modes, the implementation is based on Python and Factorie.
Collobert et al. (2011) showed that deep neural network architectures achieve state- of-the-art performance in many fundamental NLP tasks, including Named Entity Recognition (NER). However, results were only reported for English. This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER. Our system achieves an F1 -measure of 75.09% according to the official metric.
Modular Classifier Ensemble Architecture for Named Entity Recognition on Low Resource Systems
(2014)
This paper presents the best performing Named Entity Recognition system in the GermEval 2014 Shared Task. Our approach combines semi-automatically created lexical resources with an ensemble of binary classifiers which extract the most likely tag sequence. Out-of-vocabulary words are tackled with semantic generalization extracted from a large corpus and an ensemble of part-of-speech taggers, one of which is unsupervised. Unknown candidate sequences are resolved using a look-up with the Wikipedia API.
This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems.
This paper describes the process followed in creating a tool aimed at helping learners produce collocations in Spanish. First we present the Diccionario de colocaciones del español (DiCE), an online collocation dictionary, which represents the first stage of this process. The following section focuses on the potential user of a collocation learning tool: we examine the usability problems DiCE presents in this respect, and explore the actual learner needs through a learner corpus study of collocation errors. Next, we review how collocation production problems of English language learners can be solved using a variety of electronic tools devised for that language. Finally, taking all the above into account, we present a new tool aimed at assisting learners of Spanish in writing texts, with particular attention being paid to the use of collocations in this language.
Ironic speech act detection is indispensable for automatic opinion mining. This paper presents a pattern-based approach for the detection of ironic speech acts in German Web comments. The approach is based on a multilevel annotation model. Based on a gold standard corpus with labeled ironic sentences, multilevel patterns are deter- mined according to statistical and linguis- tic analysis. The extracted patterns serve to detect ironic speech acts in a Web com- ment test corpus. Automatic detection and inter-annotator results achieved by human annotators show that the detection of ironic sentences is a challenging task. However, we show that it is possible to automatically detect ironic sentences with relatively high precision up to 63%.
Virtual textual communication involves numeric supports as transporter and mediator. SMS language is part of this type of communication and represents some specific particularities. An SMS text is characterized by an unpredictable use of white-spaces, special characters and a lack of any writing standards, when at the same time stays close to the orality. This paper aims to expose the database of alpes4science project from the collation to the processing of the SMS corpus. Then we present some of the most common SMS tokenization problems and works related to SMS normalization.
