No abstract

Abstract. This paper describes the application of the perceptron algorithm to the morphological disambiguation of Turkish text. Turkish has a productive derivational morphology. Due to the ambiguity caused by complex morphology, a word may have multiple morphological parses, each with a different stem or sequence of morphemes. The methodology employed is based on ranking with perceptron algorithm which has been successful in some NLP tasks in English. We use a baseline statistical trigram-based model of a previous work to enumerate an n-best list of candidate morphological parse sequences for each sentence. We then apply the perceptron algorithm to rerank the n-best list using a set of 23 features. The perceptron trained to do morphological disambiguation improves the accuracy of the baseline model from 93.61% to 96.80%. When we train the perceptron as a POS tagger, the accuracy is 98.27%. Turkish morphological disambiguation and POS tagging results that we obtained is the best reported so far.

No abstract

BackgroundTurkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32 × -48×).ResultsWe show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.ConclusionThis study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-963) contains supplementary material, which is available to authorized users.

This work proposes a sequential tagger for named entity recognition in morphologically rich languages. Several schemes for representing the morphological analysis of a word in the context of named entity recognition are examined. Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.