Scientific theories and models in Earth science typically involve changing variables and their complex interactions, including correlations, causal relations and chains of positive/negative feedback loops.Variables tend to be complex rather than atomic entities and expressed as noun phrases containing multiple modifiers, e.g.oxygen depletion in the upper 500 m of the ocean or timing and magnitude of surface temperature evolution in the Southern Hemisphere in deglacial proxy records.Text mining from Earth science literature is therefore significantly different from biomedical text mining and requires different approaches and methods.Our approach aims at automatically locating and extracting variables and their direction of variation: increasing, decreasing or just changing.Variables are initially extracted by matching tree patterns onto the syntax trees of the source texts.Next, variables are generalised in order to enhance their similarity, facilitating hierarchical search and inference.This generalisation is accomplished by progressive pruning of syntax trees using a set of tree transformation operations.Text mining results are presented as a browsable variable hierarchy which allows users to inspect all mentions of a particular variable type in the text as well as any generalisations or specialisations.The approach is demonstrated on a corpus of 10k abstracts of Nature publications in the field of Marine science.We discuss experiences with this early prototype and outline a number of possible improvements and directions for future research.

This paper * describes an eye tracking experiment to study the processing of diphone synthesis, unit selection synthesis, and human speech taking segmental and suprasegmental speech quality into account. The results showed that both factors influenced the processing of human and synthetic speech, and confirmed that eye tracking is a promising albeit time consuming research method to evaluate synthetic speech. Index Terms: eye movements, human speech, diphone synthesis, unit selection synthesis.

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shallow, easy-to-compute features. We train the algorithms on both tasks individually and on the two tasks simultaneously. The parameters of both algorithms and the selection of features are optimized per task with iterative deepening, an efficient wrapper procedure that uses progressive sampling of training data. Results show a consistent significant advantage of MBL over CART, and also indicate that task combination can be done at the cost of little generalization score loss. Tests on cross-validated data and on held-out data yield F-scores of MBL on accent placement of 84 and 87, respectively, and on breaks of 88 and 91, respectively. Accent placement is shown to outperform an informed baseline rule; reliably predicting breaks other than those already indicated by intra-sentential punctuation, however, appears to be more challenging.

We describe results on pitch accent placement in Dutch text obtained with a memory-based learning approach. The training material consists of newspaper texts that have been prosodically annotated by humans, and subsequently enriched with linguistic features and informational metrics using generally available, lowcost, shallow, knowledge-poor tools. We report on the effects of context-modelling and the nearest neighbours parameter (k), and show the advantage of combining features of a different nature, where the best performance yields a cross-validated F-score of 82. Evaluation on an independent test corpus shows that our approach outperforms existing TTS systems for Dutch. 1.