Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Hartung M. Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns. Heidelberg: Universität Heidelberg; 2015

Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Attributes such as SIZE, WEIGHT or COLOR are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., 'big' or 'small' in case of SIZE, 'blue' or 'red' in case of COLOR). In order to ascribe such properties to entities or events, adjective-noun phrases are a very frequent linguistic pattern (e.g., 'a blue shirt', 'a big lion'). In these constructions, attribute meaning is conveyed only implicitly, i.e., without being overtly realized at the phrasal surface. This thesis is about modeling attribute meaning in adjectives and nouns in a distributional semantics framework. This implies the acquisition of meaning representations for adjectives, nouns and their phrasal combination from corpora of natural language text in an unsupervised manner, without tedious handcrafting or manual annotation efforts. These phrase representations can be used to predict implicit attribute meaning from adjective-noun phrases -- a problem which will be referred to as attribute selection throughout this thesis. The approach to attribute selection proposed in this thesis is framed in structured distributional models. We model adjective and noun meanings as distinct semantic vectors in the same semantic space spanned by attributes as dimensions of meaning. Based on these word representations, we make use of vector composition operations in order to construct a phrase representation from which the most prominent attribute(s) being expressed in the compositional semantics of the adjective-noun phrase can be selected by means of an unsupervised selection function. This approach not only accounts for the linguistic principle of compositionality that underlies adjective-noun phrases, but also avoids inherent sparsity issues that result from the fact that the relationship between an adjective, a noun and a particular attribute is rarely explicitly observed in corpora. The attribute models developed in this thesis aim at a reconciliation of the conflict between specificity and sparsity in distributional semantic models. For this purpose, we compare various instantiations of attribute models capitalizing on pattern-based and dependency-based distributional information as well as attribute-specific latent topics induced from a weakly supervised adaptation of Latent Dirichlet Allocation. Moreover, we propose a novel framework of distributional enrichment in order to enhance structured vector representations by incorporating additional lexical information from complementary distributional sources. In applying distributional enrichment to distributional attribute models, we follow the idea to augment structured representations of adjectives and nouns to centroids of their nearest neighbours in semantic space, while keeping the principle of meaning representation along structured, interpretable dimensions intact. We evaluate our attribute models in several experiments on the attribute selection task framed for various attribute inventories, ranging from a thoroughly confined set of ten core attributes up to a large-scale set of 260 attributes. Our results show that large-scale attribute selection from distributional vector representations that have been acquired in an unsupervised setting is a challenging endeavor that can be rendered more feasible by restricting the semantic space to confined subsets of attributes. Beyond quantitative evaluation, we also provide a thorough analysis of performance factors (based on linear regression) that influence the effectiveness of a distributional attribute model for attribute selection. This investigation reflects strengths and weaknesses of the model and sheds light on the impact of a variety of linguistic factors involved in attribute selection, e.g., the relative contribution of adjective and noun meaning. In conclusion, we consider our work on attribute selection as an instructive showcase for applying methods from distributional semantics in the broader context of knowledge acquisition from text in order to alleviate issues that are related to implicitness and sparsity

Learning diachronic analogies to analyze concept change

Orlikowski M, Hartung M, Cimiano P. Learning diachronic analogies to analyze concept change. In: Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics; 2018.We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly

Observation of direction instability in a fiber ring laser

We report on the observation of a new phenomenon occurring in a fiber ring laser. This phenomenon is about the transition from an initially bidirectional emission of a reciprocal fiber ring laser to a unidirectional emission at a certain pump power threshold. In addition, the final direction is not predefined but appears to be randomly chosen every time the threshold is exceeded. Therefore, we term this new phenomenon direction instability. Furthermore, we provide a first discussion of how the instability threshold is influenced by the length and the loss of the cavity. We show that the threshold follows a power times length scaling, indicating a nonlinear origin

Text Insights: Natural Language Analytics for Understanding Social Media Engagement

Grimm F, Hartung M, Cimiano P. Text Insights: Natural Language Analytics for Understanding Social Media Engagement. In: Proceedings of the SEMANTiCS 2014 Poster and Demo Track. 2014.We present Text Insights, an application for understanding factors of user engagement in Facebook pages. Providing analytics based on natural language processing, Text Insights is complementary to existing tools offering mainly numerical indicators of user engagement. Our system extracts keyphrases from page content in a linguistically motivated manner. Keyphrases are weighted according to their relevance as approximations of the most important topics in the community. We demonstrate that the system provides valuable insights for page owners interested in trend discovery, content evaluation and content planning

Experimental and Numerical Investigation of Tire Tread Wear on Block Level

Tread wear appears as a consequence of friction, which mainly depends on surface charac-teristics, contact pressure, slip velocity, temperature and dissipative material properties of the tread material itself. The subsequent description introduces a wear model as a function of the frictional energy rate. A post-processing as well as an adaptive re-meshing algorithm are implemented into a finite element code in order to predict wear loss in terms of mass. The geometry of block models is generated by image processing tools using photographs of the rubber samples in the laboratory. In addition, the worn block shape after the wear test is compared to simulation results

What Coreference Chains Tell about Experimental Groups in (Pre-)Clinical Trials

Schwitteck A, ter Horst H, Hartung M. What Coreference Chains Tell about Experimental Groups in (Pre-)Clinical Trials. In: Proceedings of DGfS/CL Poster Session. Stuttgart; 2018

Intra-cavity measurement concept of dispersion properties with a tunable fiber-integrated laser

The dispersion properties of fibers depict a key characteristic to model the propagation of ultra-short pulses in waveguides. In the following, a new method is presented to directly measure the dispersion properties of fibers and optical components in the time domain. The analysis is based on pulse shape variations along the tuning range of a theta cavity fiber laser (TCFL) depending on the adjusted repetition rate. The automated measurement procedure, evaluating pulse symmetry, achieves a temporal sensitivity below 5 ps surpassing the resolution of the acquisition electronics. Exemplarily, two samples of Nufern PM980-XP fiber are investigated with an Yb-doped tunable TCFL retrieving the mean dispersion parameter D? by comparative measurements. The obtained results are compared to a reference method based on spectral interferometry. With deviations in D? between either approach of 0.3% and 1.3%, respectively, the results agree well within the measurement errors of the TCFL, verifying the presented concept. Due to the pulse formation process extending over multiple round trips, this approach achieves an enhanced sensitivity compared to competing direct temporal methods. Together with an alignment free operation, the fiber-integrated TCFL depicts a simple and robust concept showing potential in specific measurement scenarios such as in quality management. © 2019 Astro Ltd