Cem Akkaya


Hierarchical Transfer Learning for Multi-label Text Classification
Siddhartha Banerjee | Cem Akkaya | Francisco Perez-Sorrosal | Kostas Tsioutsiouliklis
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category. We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. In HTrans, we use a Gated Recurrent Unit (GRU)-based deep learning architecture coupled with attention. Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1% on micro-F1 and 3% on macro-F1 on the RCV1 dataset. Our experiments also show that binary classifiers trained from scratch are significantly better than single multi-label models.


Identifying Domain Independent Update Intents in Task Based Dialogs
Prakhar Biyani | Cem Akkaya | Kostas Tsioutsiouliklis
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA’s fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI’s, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM’s to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.


First Story Detection using Entities and Relations
Nikolaos Panagiotou | Cem Akkaya | Kostas Tsioutsiouliklis | Vana Kalogeraki | Dimitrios Gunopulos
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable and accurate First Story Detection (FSD) pipeline that identifies fresh news. In comparison to other FSD systems, our method relies on relation extraction methods exploiting entities and their relations. We evaluate our pipeline using two distinct datasets from Yahoo News and Google News. Experimental results demonstrate that our method improves over the state-of-the-art systems on both datasets with constant space and time requirements.


Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation
Cem Akkaya | Janyce Wiebe | Rada Mihalcea
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics


Improving the Impact of Subjectivity Word Sense Disambiguation on Contextual Opinion Analysis
Cem Akkaya | Janyce Wiebe | Alexander Conrad | Rada Mihalcea
Proceedings of the Fifteenth Conference on Computational Natural Language Learning


Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
Cem Akkaya | Alexander Conrad | Janyce Wiebe | Rada Mihalcea
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk


Subjectivity Word Sense Disambiguation
Cem Akkaya | Janyce Wiebe | Rada Mihalcea
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Integrating Knowledge for Subjectivity Sense Labeling
Yaw Gyamfi | Janyce Wiebe | Rada Mihalcea | Cem Akkaya
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics