Automatic identification of pronominal anaphora in Turkish texts

Date

2007-11-09

Author

Kucuk, Dilek
Yondem, Meltem Turhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

99
views

0
downloads

Anaphora identification is an important problem especially for its impact on anaphora and coreference resolution systems. In this paper, a system that automatically identifies anaphoric pronouns in Turkish is presented. The proposed system takes a decision tree learning approach, that of Quinlan's C 4.5, where a corpus examination is carried out to determine linguistic features specific to Turkish which are to be used by the decision tree learner. The proposed system is significant especially for its ease of incorporation into any anaphora resolution system for Turkish. The system is evaluated on two different Turkish text samples and its performance on these samples is close to that of human identification.

Subject Keywords

Decision trees, Usability, Data preprocessing , Machine learning , Natural languages, Information retrieval, Natural language processing, Humans

URI

https://hdl.handle.net/11511/64506

DOI

https://doi.org/10.1109/iscis.2007.4456858

Conference Name

22nd International Symposium on Computer and Information Sciences

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery Sumbul, Gencer; Cinbiş, Ramazan Gökberk; Aksoy, Selim (2018-02-01) Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low betweenclass variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists f...
A METHOD FOR COMPARATIVE-EVALUATION OF EIT ALGORITHMS USING A STANDARD DATA SET IDER, YZ; EYUBOGLU, BM; KUZUOGLU, M; Leblebicioğlu, Mehmet Kemal; BAYSAL, U; CAGLAR, BK; BIRGUL, O (IOP Publishing, 1995-08-01) The point spread function (PSF) is the most widely used tool for quantifying the spatial resolution of imaging systems. However, prerequisites for the proper use of this tool are linearity and space invariance. Because EIT is non-linear it is only possible to compare different reconstruction algorithms using a standard data set. In this study, the FEM is used to generate simulation data, which are used to investigate the non-linear behaviour of EIT, the space dependence of its PSF and its capability of reso...
Distributed restoration in optical networks using feed-forward neural networks Karpat, Demeter Gokisik; Bilgen, Semih (Springer Science and Business Media LLC, 2006-07-01) A new method is proposed for determining protection paths in an optical network where users have different characteristics in terms of reliability needs and security restrictions. Survivability is achieved by distributed mesh protection. Over the preplanned primary and backup capacity, optimal routing and wavelength assignment is carried out. In case of a network failure, protection routes and optimum flow values on these protection routes are extracted from a previously trained feed-forward neural network ...
A Computational approach to detect inhomogeneities in time series data Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci; Department of Statistics (2017) Detection of possible inhomogeneity within a series is an important problem in time series data. There are many sources from which inhomogeneity can be originated such as mean shift, variance and trend change, gradual change, or sudden decrease or increase in time series. Since time series has many application areas, the detection of changepoints should be investigated before conducting any analysis. Available methods have certain drawbacks that may lead to unreliable inferences. These include the need of i...
The model selection methods for sparse biological networks Purutçuoğlu Gazi, Vilda (null; 2019-10-30) It is still crucial problem to estimate high dimensional graphical models and to choose the regularization parameter in dependent data. There are several classical methods such as Akaike’s information criterion and Bayesian Information criterion to solve this problem, but also more recent methods have been proposed such as stability selection and stability approach to regularization selection method (StARS) and some extensions of AIC and BIC which are more appropriate for high dimensional datasets. In this ...

Citation Formats

D. Kucuk and M. T. Yondem, “Automatic identification of pronominal anaphora in Turkish texts,” Ankara, TURKEY, 2007, p. 180, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64506.