anna.korhonen

We present a novel approach to automatic metaphor identification in unrestricted text. Starting from a small seed set of manually annotated metaphorical expressions, the system is capable of harvesting a large number of metaphors of similar syntactic structure from a corpus. Our method is distinguished from previous work in that it does not employ any hand-crafted knowledge, other than the initial seed set, but, in contrast, captures metaphoricity by means of verb and noun clustering. Being the first to employ unsupervised methods for metaphor identification, our system operates with the precision of 0.79. 1

Argumentative Zoning (AZ) – analysis of the argumentative structure of a scientific paper – has proved useful for a number of information access tasks. Current approaches to AZ rely on supervised machine learning (ML). Requiring large amounts of annotated data, these approaches are expensive to develop and port to different domains and tasks. A potential solution to this problem is to use weaklysupervised ML instead. We investigate the performance of four weakly-supervised classifiers on scientific abstract data annotated for multiple AZ classes. Our best classifier based on the combination of active learning and selftraining outperforms our best supervised classifier, yielding a high accuracy of 81 % when using just 10 % of the labeled data. This result suggests that weakly-supervised learning could be employed to improve the practical applicability and portability of AZ across different information access tasks. 1

Abstract. Verb classifications have been used to support a number of practical tasks and applications, such as parsing, information extraction, question-answering, and machine translation. However, large-scale exploitation of verb classes in real-world or domain-sensitive tasks has not been possible because existing manually built classifications are incomprehensive. This paper describes recent and on-going research on extending and acquiring lexical classifications automatically. The automatic approach is attractive since it is cost-effective and opens up the opportunity of learning and tuning lexical classifications for the application and domain in question. However, the development of an optimal approach is challenging, and requires not only expertise in machine learning but also a good understanding of the linguistic principles of lexical classification.

Many practical tasks require accessing specific types of information in scientific literature; e.g. information about the objective, methods, results or conclusions of the study in question. Several schemes have been developed to characterize such information in full journal papers. Yet many tasks focus on abstracts instead. We take three schemes of different type and granularity (those based on section names, argumentative zones and conceptual structure of documents) and investigate their applicability to biomedical abstracts. We show that even for the finest-grained of these schemes, the majority of categories appear in abstracts and can be identified relatively reliably using machine learning. We discuss the impact of our results and the need for subsequent task-based evaluation of the schemes. 1

Most previous research on verb clustering has focussed on acquiring flat classifications from corpus data, although many manually built classifications are taxonomic in nature. Also Natural Language Processing (NLP) applications benefit from taxonomic classifications because they vary in terms of the granularity they require from a classification. We introduce a new clustering method called Hierarchical Graph Factorization Clustering (HGFC) and extend it so that it is optimal for the task. Our results show that HGFC outperforms the frequently used agglomerative clustering on a hierarchical test set extracted from VerbNet, and that it yields state-of-the-art performance also on a flat test set. We demonstrate how the method can be used to acquire novel classifications as well as to extend existing ones on the basis of some prior knowledge about the classification. 1

Evaluating automatic annotation: automatically detecting and enriching instances of the dative alternation