Bootstrapping Information Extraction from Field Books

We present two machine learning approaches to information extraction from semi-structured documents that can be used if no annotated training data are available, but there does exist a database filled with information derived from the type of documents to be processed. One approach employs standard supervised learning for information extraction by artificially constructing labelled training data from the contents of the database. The second approach combines unsupervised Hidden Markov modelling with language models. Empirical evaluation of both systems suggests that it is possible to bootstrap a field segmenter from a database alone. The combination of Hidden Markov and language modelling was found to perform best at this task.

Discovering Lexical Generalisations. A Supervised Machine Learning Approach to Inheritance Hierarchy Construction

Institute for Communicating and Collaborative SystemsGrammar development over the last decades has seen a shift away from large inventories of grammar rules to richer lexical structures. Many modern grammar theories are highly lexicalised. But simply listing lexical entries typically results in an undesirable amount of redundancy. Lexical inheritance hierarchies, on the other hand, make it possible to capture linguistic generalisations and thereby reduce redundancy. Inheritance hierarchies are usually constructed by hand but this is time-consuming and often impractical if a lexicon is very large. Constructing hierarchies automatically or semiautomatically facilitates a more systematic analysis of the lexical data. In addition, lexical data is often extracted automatically from corpora and this is likely to increase over the coming years. Therefore it makes sense to go a step further and automate the hierarchical organisation of lexical data too. Previous approaches to automatic lexical inheritance hierarchy construction tended to focus on minimality criteria, aiming for hierarchies that minimised one or more criteria such as the number of path-value pairs, the number of nodes or the number of inheritance links (Petersen 2001, Barg 1996a, and in a slightly different context: Light 1994). Aiming for minimality is motivated by the fact that the conciseness of inheritance hierarchies is a main reason for their use. However, I will argue that there are several problems with minimality-based approaches. First, minimality is not well defined in the context of lexical inheritance hierarchies as there is a tension between different minimality criteria. Second, minimality-based approaches tend to underestimate the importance of linguistic plausibility. While such approaches start with a definition of minimal redundancy and then try to prove that this leads to plausible hierarchies, the approach suggested here takes the opposite direction. It starts with a manually built hierarchy to which a supervised machine learning algorithm is applied with the aim of finding a set of formal criteria that can guide the construction of plausible hierarchies. Taking this direction means that it is more likely that the selected criteria do in fact lead to plausible hierarchies. Using a machine learning technique also has the advantage that the set of criteria can be much larger than in hand-crafted definitions. Consequently, one can define conciseness in very broad terms, taking into account interdependencies in the data as well as simple minimality criteria. This leads to a more fine-grained model of hierarchy quality. In practice, the method proposed here consists of two components: Galois lattices are used to define the search space as the set of all generalisations over the input lexicon. Maximum entropy models which have been trained on a manually built hierarchy are then applied to the lattice of the input lexicon to distinguish between plausible and implausible generalisations based on the formal criteria that were found in the training step. An inheritance hierarchy is then derived by pruning implausible generalisations. The hierarchy is automatically evaluated by matching it to a manually built hierarchy for the input lexicon. Automatically constructing lexical hierarchies is a hard task, partly because what is considered the best hierarchy for a lexicon is to some extent subjective. Supervised learning methods also suffer from a lack of suitable training data. Hence, a semi-automatic architecture may be best suited for the task. Therefore, the performance of the system has been tested using a semi-automatic as well as an automatic architecture and it has also been compared to the performance achieved by the pruning algorithm suggested by Petersen (2001). The findings show that the method proposed here is well suited for semi-automatic hierarchy construction

So to Speak: A Computational and Empirical Investigation of Lexical Cohesion of Non-Literal and Literal Expressions in Text

Lexical cohesion is an important device for signaling text organization. In this paper, we investigate to what extent a particular class of expressions which can have a non-literal interpretation participates in the cohesive structure of a text. Specifically, we look at five expressions headed by a verb which – depending on the context – can have either a literal or a non-literal meaning: bounce off the wall (“to be excited and full of nervous energy”), get one’s feet wet (“to start a new activity or job”), rock the boat (“to disturb the balance or routine of a situation”), break the ice (“to start to get to know people, to overcome initial shyness”), and play with fire (“to take part in a dangerous or risky undertaking”). We look at the problem both from an empirical and a computational perspective. The results from our empirical study suggest that both literal and non-literal expressions exhibit cohesion with their textual context, but that the latter appear to do so to a lesser extent. We also show that an automatically computable semantic relatedness measure based on search engine page counts correlates well with human intuitions about the cohesive structure of a text and can therefore be used to determine the cohesive structure of a text automatically with a reasonable degree of accuracy. This investigation is undertaken from the perspective of computational linguistics. We aim both to model this cohesion computationally and to support our approach to computational modeling with empirical data

Preface

Preface of the proceedings of the international workshop ACRH-

The (In-)Consistency of Literary Concepts. Operationalising, Annotating and Detecting Literary Comment

This paper explores how both annotation procedures and automatic detection (i.e. classifiers) can be used to assess the consistency of textual literary concepts. We developed an annotation tagset for the ‘literary comment’ – a frequently used but rarely defined concept – and its subtypes (interpretative comment, attitude comment and metanarrative/metafictional comment) and trained a multi-output and a binary classifier. The multi-output classifier shows F-scores of 28% for attitude comment, 36% for interpretative comment and 48% for meta comment, whereas the binary classifier achieves F-scores up to 59%. Crucially, both our annotation and the automatic classification struggle with the same subtypes of comment, although annotation and classification follow completely different procedures. Our findings suggest an inconsistency in the overall literary concept ‘comment’ and most prominently the subtypes ‘attitude comment’ and ‘interpretative comment’. As a best-practice-example, our approach illustrates that the contribution of Digital Humanities to Literary Studies may go beyond the automatic recognition of literary phenomena