The main task of part-of-speech (PoS) tagging is to assign the appropriate morphosyntactic category to each word in a sentence. A combination of different PoS taggers usually results in higher tagging accuracy than obtained by the use of only a single tagger. We present a new language and tagset independent system, CombiTagger, which combines automatically the output of several taggers. The system, which is open source, provides algorithms for simple and weighted voting, but it is extensible so that other combination algorithms can be added easily. We demonstrate the functionality of CombiTagger by using it to develop and evaluate combined taggers for Icelandic. The most accurate individual tagger obtains an accuracy of 91.83%. CombiTagger achieves 93.09%-93.41% accuracy by combining the output of five or six taggers using simple and weighted voting.

This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functional modules, each having a limited domain knowledge captured by a local Bayesian network. Different modules originate from independent design processes. We assume that the local models are correct, which, however does not guarantee globally coherent inference in composed systems. The introduced method supports discovery of significant inter module dependencies which are ignored in the assembled Bayesian network.

While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions.  This includes recognizing the current state of the action, predicting the next state, recognizing deviations from the standard action model, and handling ambiguous cases. The underlying probabilistic reasoning model has a major impact on the type of data it can extract, its accuracy, and the computational cost of the reasoning process. In this paper we are using Cascading Hidden Markov Models (CHMM) to analyze Bounding Overwatch, an important team action in military tactics. The team action is represented in the CHMM as a plan tree. Starting from real-world recorded data, we identify the subteams through clustering and extract team oriented discrete features. In an experimental study, we investigate whether the better scalability and the more structured information provided by the CHMM comes with an unacceptable cost in accuracy. We find the a properly parametrized CHMM estimating the current goal chain of the Bounding Overwatch plan tree comes very close to a flat HMM estimating only the overall Bounding Overwatch state (a subset of the goal chain) at a respective overall state accuracy of 95% vs 98%, making the CHMM a good candidate for deployed systems.

A general practice in the research of metaphor has been to investigate its behavior and function in different contexts. This current study aims to investigate the notion that verbs possess a metaphor-making potential, this being an initiatory context-free experiment with metaphor. The goal of this paper is to carry out an in-depth case study of a group of English core verbs using WordNet and SUMO ontology. In order to operationalize the measurement of an English verb’s metaphor making potential, a new algorithm has been developed, and a program made to realize the computation. At last, it has been observed that higher frequency verbs generally possess greater metaphor making potential; while a verb’s metaphor making potential on the other hand is also strongly influenced by its functional category.

This study evaluates the robustness of established computational indices used to assess text relatedness in user-language. The original User-Language Paraphrase Corpus (ULPC) was compared to a corrected version, in which each paraphrase was corrected for typographical and grammatical errors. Error correction significantly affected values for each of five computational indices, indicating greater similarity of the target sentence to the corrected paraphrase than to the original paraphrase. Moreover, misspelled target words accounted for a large proportion of the differences. This study also evaluated potential effects on correlations between computational indices and human ratings of paraphrases. The corrections did not yield assessments that were any more or less comparable to trained human raters than were the original paraphrases containing typographical or grammatical errors. The results suggest that although correcting for errors may optimize certain computational indices, the corrections are not necessary for comparing the indices to expert ratings.

Generalized cases are cases that cover a subspace rather than a point in the problem-solution space. Attribute dependent generalized cases are a subclass of generalized cases, which cause a high computational complexity during similarity assessment. We present a new approach for an efficient index-based retrieval of such generalized cases by an improved kd-tree approach. The experimental evaluation demonstrates a significant improvement in retrieval efficiency compared to previous methods.

In this paper certain knowledge and software engineering methods integration issues are discussed. The principal idea is to consider an effective design and implementation framework for rule design with UML, and implementation with Java. The solution proposed in the paper consists of using a custom knowledge engineering design method for rules in the design stage. The rule base is then transformed to UML behavioral diagrams, which can be considered a visual encoding. The rule implementation involves the serialization to Java language using classes representing the decision tables grouping rules sharing the same attributes.

Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), involve an iterative process of model optimization to identify different types of words or semantic entities. This optimization to achieve a more precise model becomes computationally difficult as the number of iterations increase. The small datasets available for training typically limit the models. Adding iterations on such sets to further optimize the model can often cause over-fitting, which generally leads to reduced performance. Therefore, the choice of convergence criteria is a critical step in robust and accurate model building. We evaluate different convergence criteria in terms of their robustness, stopping threshold selection, and independence from the training data size and entity. The underlying framework employs a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) parameter optimization in the context of Conditional Random Fields (CRF). This paper presents a convergence criterion for robust training irrespective of semantic types and data sizes with two-orders of magnitude reduction in stopping threshold for improved model accuracy and faster convergence. Additionally, we examine convergence with active learning to further reduce the training data and training time.

Providing the first few letters of a missing word in a sentence gives information about this word. This paper attempts to measure the information transmitted in that case. In order to do so, we analyzed response accuracy for open cloze questions, that is fill-in-the-blank questions without multiple choice answers. In this study, native and non-native speakers of English answered a series of open cloze questions that were semi-automatically generated. Hints were provided that consisted of the first few letters of the missing word. Results showed that question difficulty, hence the quantity of information transmitted, is related to the number of letters that are provided, to physical properties of these letters and to syllables formed by these letters. Performances did not appear to depend on letter or syllable frequency. Controlling hint level in a word completion task is critical in order to provide practice exercises adapted to student levels.

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