Teemu Ruokolainen

An augmented reality interface to contextual information

In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user’s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user’s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.

Contextual information access with Augmented Reality

We have developed a prototype platform for contextual information access in mobile settings. Objects, people, and the environment are considered as contextual channels or cues to more information. The system infers, based on gaze, speech and other implicit feedback signals, which of the contextual cues are relevant, retrieves more information relevant to the cues, and presents the information with Augmented Reality (AR) techniques on a handheld or head-mounted display. The augmented information becomes potential contextual cues as well, and its relevance is assessed to provide more information. In essence, the platform turns the real world into an information browser which focuses proactively on the information inferred to be the most relevant for the user. We present the first pilot application, a Virtual Laboratory Guide, and its early evaluation results.

Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random field model via feature set augmentation. Experiments on three diverse languages show that this straightforward semi-supervised extension greatly improves the segmentation accuracy of the purely supervised CRFs in a computationally efficient manner.

FinnPos: an open-source morphological tagging and lemmatization toolkit for Finnish

This paper describes FinnPos, an open-source morphological tagging and lemmatization toolkit for Finnish. The morphological tagging model is based on the averaged structured perceptron classifier. Given training data, new taggers are estimated in a computationally efficient manner using a combination of beam search and model cascade. The lemmatization is performed employing a combination of a rule-based morphological analyzer, OMorFi, and a data-driven lemmatization model. The toolkit is readily applicable for tagging and lemmatization of running text with models learned from the recently published Finnish Turku Dependency Treebank and FinnTreeBank. Empirical evaluation on these corpora shows that FinnPos performs favorably compared to reference systems in terms of tagging and lemmatization accuracy. In addition, we demonstrate that our system is highly competitive with regard to computational efficiency of learning new models and assigning analyses to novel sentences.

A comparative study of minimally supervised morphological segmentation

This article presents a comparative study of a subfield of morphology learning referred to as minimally supervised morphological segmentation. In morphological segmentation, word forms are segmented into morphs, the surface forms of morphemes. In the minimally supervised data-driven learning setting, segmentation models are learned from a small number of manually annotated word forms and a large set of unannotated word forms. In addition to providing a literature survey on published methods, we present an in-depth empirical comparison on three diverse model families, including a detailed error analysis. Based on the literature survey, we conclude that the existing methodology contains substantial work on generative morph lexicon-based approaches and methods based on discriminative boundary detection. As for which approach has been more successful, both the previous work and the empirical evaluation presented here strongly imply that the current state of the art is yielded by the discriminative boundary detection methodology.

Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy

We discuss part-of-speech (POS) tagging in presence of large, fine-grained label sets using conditional random fields (CRFs). We propose improving tagging accuracy by utilizing dependencies within sub-components of the fine-grained labels. These sub-label dependencies are incorporated into the CRF model via a (relatively) straightforward feature extraction scheme. Experiments on five languages show that the approach can yield significant improvement in tagging accuracy in case the labels have sufficiently rich inner structure.

Applying piecewise approximation in perceptron training of conditional random fields

We show that the recently proposed piecewise approximation approach can benefit conditional random fields estimation using the structured perceptron algorithm. We present experiments in noun-phrase chunking task on the CoNLL-2000 corpus. The results show that, compared to standard training, applying the piecewise approach during model estimation may yield not only savings in training time but also improvement in model performance on test set due to added model regularization.

Accelerated Estimation of Conditional Random Fields using a Pseudo-Likelihood-inspired Perceptron Variant

We discuss a simple estimation approach for conditional random fields (CRFs). The approach is derived heuristically by defining a variant of the classic perceptron algorithm in spirit of pseudo-likelihood for maximum likelihood estimation. The resulting approximative algorithm has a linear time complexity in the size of the label set and contains a minimal amount of tunable hyper-parameters. Consequently, the algorithm is suitable for learning CRFbased part-of-speech (POS) taggers in presence of large POS label sets. We present experiments on five languages. Despite its heuristic nature, the algorithm provides surprisingly competetive accuracies and running times against reference methods.