A hybrid machine translation system from Turkish to English

Machine Translation (MT) is the process of automatically transforming a text in one natural language into an equivalent text in another natural language, so that the meaning is preserved. Even though it is one of the first applications of computers, state-of-the-art systems are far from being an alternative to human translators. Nevertheless, the demand for translation is increasing and the supply of human translators is not enough to satisfy this demand. International corporations, organizations, universities, and many others need to deal with different languages in everyday life, which creates a need for translation. Therefore, MT systems are needed to reduce the effort and cost of translation, either by doing some of the translations, or by assisting human translators in some ways. In this work, we introduce a hybrid machine translation system from Turkish to English, by combining two different approaches to MT. Transfer-based approaches have been successful at expressing the structural differences between the source and target languages, while statistical approaches have been useful at extracting relevant probabilistic models from huge amounts of parallel text that would explain the translation process. The hybrid approach transfers a Turkish sentence to all of its possible English translations, using a set of manually written transfer rules. Then, it uses a probabilistic language model to pick the most probable translation out of this set. We have evaluated our system on a test set of Turkish sentences, and compared the results to reference translations

Solving challenging grid puzzles with answer set programming

We study four challenging grid puzzles, Nurikabe, Heyawake, Masyu, Bag Puzzle, interesting for answer set programming (ASP) from the viewpoints of representation and computation: they show expressivity of ASP, they are good examples of a representation methodology, and they form a useful suite of benchmarks for evaluating/improving computational methods for nontight programs

Efficient haplotype inference with answer set programming

Identifying maternal and paternal inheritance is essential to be able to find the set of genes responsible for a particular disease. Although we have access to genotype data (genetic makeup of an individual), determining haplotypes (genetic makeup of the parents) experimentally is a costly and time consuming procedure due to technological limitations. With these biological motivations, we study a computational problem, called Haplotype Inference with Pure Parsimony (HIPP), that asks for the minimal number of haplotypes that form a given set of genotypes. We introduce a novel approach to solving HIPP, using Answer Set Programming (ASP). According to our experiments with a large number of problem instances (some automatically generated and some real), our ASP-based approach solves the most number of problems compared to other approaches based on, e.g., integer linear programming, branch and bound algorithms, SAT-based algorithms, or pseudo-boolean optimization methods

F.: Learning Morphological Disambiguation Rules for Turkish

In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm using supervised training. Morphological ambiguity (e.g. lives = live+s or life+s) is a challenging problem for agglutinative languages like Turkish where close to half of the words in running text are morphologically ambiguous. Furthermore, it is possible for a word to take an unlimited number of suffixes, therefore the number of possible morphological tags is unlimited. We attempted to cope with these problems by training a separate model for each of the 126 morphological features recognized by the morphological analyzer. The resulting decision lists independently vote on each of the potential parses of a word and the final parse is selected based on our confidence on these votes. The accuracy of our model (96%) is slightly above the best previously reported results which use statistical models. For comparison, when we train a single decision list on full tags instead of using separate models on each feature we get 91 % accuracy.

Comparing ASP, CP, ILP on two challenging applications: wire routing and haplotype inference

We study three declarative programming paradigms, Answer Set Programming (ASP), Constraint Programming (CP), and Integer Linear Programming (ILP), on two challenging applications: wire routing and haplotype inference. We represent these problems in each formalism in a systematic way, compare the formulations both from the point of view of knowledge representation (e.g., how tolerant they are to elaborations) and from the point of view of computational efficiency (in terms of computation time and program size). We discuss possible ways of improving the computational efficiency, and other reformulations of the problems based on different mathematical models

Comparing ASP, CP, ILP on two challenging applications: wire routing and haplotype inference

We study three declarative programming paradigms, Answer Set Programming (ASP), Constraint Programming (CP), and Integer Linear Programming (ILP), on two challenging applications: wire routing and haplotype inference. We represent these problems in each formalism in a systematic way, compare the formulations both from the point of view of knowledge representation (e.g., how tolerant they are to elaborations) and from the point of view of computational efficiency (in terms of computation time and program size). We discuss possible ways of improving the computational efficiency, and other reformulations of the problems based on different mathematical models