Christian Hänig

Also published as: Christian Haenig


ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity
Christian Hänig | Robert Remus | Xose De La Puente
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


PACE Corpus: a multilingual corpus of Polarity-annotated textual data from the domains Automotive and CEllphone
Christian Haenig | Andreas Niekler | Carsten Wuensch
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we describe a publicly available multilingual evaluation corpus for phrase-level Sentiment Analysis that can be used to evaluate real world applications in an industrial context. This corpus contains data from English and German Internet forums (1000 posts each) focusing on the automotive domain. The major topic of the corpus is connecting and using cellphones to/in cars. The presented corpus contains different types of annotations: objects (e.g. my car, my new cellphone), features (e.g. address book, sound quality) and phrase-level polarities (e.g. the best possible automobile, big problem). Each of the posts has been annotated by at least four different annotators ― these annotations are retained in their original form. The reliability of the annotations is evaluated by inter-annotator agreement scores. Besides the corpus data and format, we provide comprehensive corpus statistics. This corpus is one of the first lexical resources focusing on real world applications that analyze the voice of the customer which is crucial for various industrial use cases.


Knowledge-free Verb Detection through Tag Sequence Alignment
Christian Hänig
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)


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Improvements in Unsupervised Co-Occurrence Based Parsing
Christian Hänig
Proceedings of the Fourteenth Conference on Computational Natural Language Learning


UnsuParse: unsupervised Parsing with unsupervised Part of Speech Tagging
Christian Hänig | Stefan Bordag | Uwe Quasthoff
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Based on simple methods such as observing word and part of speech tag co-occurrence and clustering, we generate syntactic parses of sentences in an entirely unsupervised and self-inducing manner. The parser learns the structure of the language in question based on measuring “breaking points” within sentences. The learning process is divided into two phases, learning and application of learned knowledge. The basic learning works in an iterative manner which results in a hierarchical constituent representation of the sentence. Part-of-Speech tags are used to circumvent the data sparseness problem for rare words. The algorithm is applied on untagged data, on manually assigned tags and on tags produced by an unsupervised part of speech tagger. The results are unsurpassed by any self-induced parser and challenge the quality of trained parsers with respect to finding certain structures such as noun phrases.