A Novel Kernel for Text Classification Based on Semantic and Statistical Information

In text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Metinsel veri madenciliği için anlamsal yarı-eğitimli algoritmaların geliştirilmesi

Ganiz, Murat Can (Dogus Author) -- Zeynep Hilal, Kilimci (Dogus Author)Metinsel veri madenciliği büyük miktarlardaki metinsel verilerden faydalı bilgilerin çıkarılması veya bunların otomatik olarak organize edilmesini içerir. Büyük miktarlarda metinsel belgenin otomatik olarak organize edilmesinde metin sınıflandırma algoritmaları önemli bir rol oynar. Bu alanda kullanılan sınıflandırma algoritmaları “eğitimli” (supervised), kümeleme algoritmaları ise “eğitimsiz” (unsupervised) olarak adlandırılırlar. Bunların ortasında yer alan “yarı-eğitimli” (semisupervised) algoritmalar ise etiketli verinin yanı sıra bol miktarda bulunan etiketsiz veriden faydalanarak sınıflandırma başarımını arttırabilirler. Metinsel veri madenciliği algoritmalarında geleneksel olarak kelime sepeti (bag-of-words) olarak tabir edilen model kullanılmaktadır. Kelime sepeti modeli metinde geçen kelimeleri bulundukları yerden ve birbirinden bağımsız olarak değerlendirir. Ayrıca geleneksel algoritmalardaki bir başka varsayım ise metinlerin birbirinden bağımsız ve eşit olarak dağıldıklarıdır. Sonuç olarak bu yaklaşım tarzı kelimelerin ve metinlerin birbirleri arasındaki anlamsal ilişkileri göz ardı etmektedir. Metinsel veri madenciliği alanında son yıllarda özellikle kelimeler arasındaki anlamsal ilişkilerden faydalanan çalışmalara ilgi artmaktadır. Anlamsal bilginin kullanılması geleneksel makine öğrenmesi algoritmalarının başarımını özellikle eldeki verinin az, seyrek veya gürültülü olduğu durumlarda arttırmaktadır. Gerçek hayat uygulamalarında algoritmaların eğitim için kullanacağı veri genellikle sınırlı ve gürültülüdür. Bu yüzden anlamsal bilgiyi kullanabilen algoritmalar gerçek hayat problemlerinde büyük yarar sağlama potansiyeline sahiptir. Bu projede, ilk aşamada eğitimli metinsel veri madenciliği için anlamsal algoritmalar geliştirdik. Bu anlamsal algoritmalar metin sınıflandırma ve özellik seçimi alanlarında performans artışı sağlamaktadır. Projenin ikinci aşamasında ise bu yöntemlerden yola çıkarak etiketli ve etiketsiz verileri kullanan yarı-eğitimli metin sınıflandırma algoritmaları geliştirme faaliyetleri yürüttük. Proje süresince 5 yüksek lisans tezi tamamlanmış, 1 Doktora tezi tez savunma aşamasına gelmiş, 2 adet SCI dergi makalesi yayınlanmış, 8 adet bildiri ulusal ve uluslararası konferanslar ve sempozyumlarda sunulmuş ve yayınlanmıştır. Hazırlanan 2 adet dergi makalesi ise dergilere gönderilmiş ve değerlendirme aşamasındadır. Projenin son aşamasındaki bulgularımızı içeren 1 adet konferans bildirisi 2 adet dergi makalesi de hazırlık aşamasındadır. Ayrıca proje ile ilgili olarak üniversite çıkışlı bir girişim şirketi (spin-off) kurulmuştur.Textual data mining is the process of extracting useful knowledge from large amount of textual data. In this field, classification algorithms are called supervised and clustering algorithms are called unsupervised algorithms. Between these there are semi supervised algorithms which can improve the accuracy of the classification by making use of the unlabeled data. Traditionally, bag-of-words model is being used in textual data mining algorithms. Bag-of-words model assumes that words independent from each other and their positions in the text. Furthermore, traditional algorithms assume that texts are independent and identically distributed. As a result this approach ignores the semantic relationship between words and between texts. There has been a recent interest in works that make use of the semantic relationships especially between the words. Use of semantic knowledge increase the performance of the systems especially when there are few, sparse and noisy data. In fact, there are very sparse and noisy data in real world settings. As a result, algorithms that can make use of the semantic knowledge have a great potential to increase the performance. In this project, in the first phase, we developed semantic algorithms and methods for supervised classification. These semantic algorithms provide performance improvements on text classification and feature selection. On the second phase of the project we have pursued development activities for semi-supervised classification algorithms that make use of labeled and unlabeled data, based on the methods developed in the first phase. During the project, 5 master’s thesis is completed, the PhD student is advanced to the dissertation defense stage, two articles are published on SCI indexed journals, 8 proceedings are presented in national and international conferences. Two journal articles are sent and 1 conference proceeding and two journal articles are in preparation, which include the findings of the last phase of the project. Furthermore, a spin-off technology company is founded related to the project.TÜBİTA

Protein interaction sentence detection using multiple semantic kernels

<p>Abstract</p> <p>Background</p> <p>Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection.</p> <p>Results</p> <p>We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels.</p> <p>Conclusions</p> <p>The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins.</p

Semantic models as metrics for kernel-based interaction identification

Automatic detection of protein-protein interactions (PPIs) in biomedical publications is vital for efficient biological research. It also presents a host of new challenges for pattern recognition methodologies, some of which will be addressed by the research in this thesis. Proteins are the principal method of communication within a cell; hence, this area of research is strongly motivated by the needs of biologists investigating sub-cellular functions of organisms, diseases, and treatments. These researchers rely on the collaborative efforts of the entire field and communicate through experimental results published in reviewed biomedical journals. The substantial number of interactions detected by automated large-scale PPI experiments, combined with the ease of access to the digitised publications, has increased the number of results made available each day. The ultimate aim of this research is to provide tools and mechanisms to aid biologists and database curators in locating relevant information. As part of this objective this thesis proposes, studies, and develops new methodologies that go some way to meeting this grand challenge. Pattern recognition methodologies are one approach that can be used to locate PPI sentences; however, most accurate pattern recognition methods require a set of labelled examples to train on. For this particular task, the collection and labelling of training data is highly expensive. On the other hand, the digital publications provide a plentiful source of unlabelled data. The unlabelled data is used, along with word cooccurrence models, to improve classification using Gaussian processes, a probabilistic alternative to the state-of-the-art support vector machines. This thesis presents and systematically assesses the novel methods of using the knowledge implicitly encoded in biomedical texts and shows an improvement on the current approaches to PPI sentence detection