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

Analyzing evolution of rare events through social media data

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering algorithm-based data processing method to characterize the evolution of a rare event by analyzing social media data. Secondly, novel feature extraction and fuzzy logic-based classification methods are proposed to distinguish and classify event-related and unrelated messages. Lastly, since many messages do not have ground truth, we execute four existing ground-truth inference algorithms to deduce the ground truth and compare their performances. Then, an Adaptive Majority Voting (Adaptive MV) method is proposed and compared with two of the existing algorithms based on a set containing manually-labeled social media data. Our case studies focus on Hurricane Sandy in 2012 and Hurricane Maria in 2017. Twitter data collected around them are used to verify the effectiveness of the proposed methods. Firstly, the results of the proposed data processing method not only verify that a rare event and social media activities have strong correlations, but also reveal that they have some time difference. Thus, it is conducive to investigate the temporal pattern of social media activities. Secondly, fuzzy logic-based feature extraction and classification methods are effective in identifying event-related and unrelated messages. Lastly, the Adaptive MV method deduces the ground truth well and performs better on datasets with noisy labels than other two methods, Positive Label Frequency Threshold and Majority Voting

Introspective knowledge acquisition for case retrieval networks in textual case base reasoning.

Textual Case Based Reasoning (TCBR) aims at effective reuse of information contained in unstructured documents. The key advantage of TCBR over traditional Information Retrieval systems is its ability to incorporate domain-specific knowledge to facilitate case comparison beyond simple keyword matching. However, substantial human intervention is needed to acquire and transform this knowledge into a form suitable for a TCBR system. In this research, we present automated approaches that exploit statistical properties of document collections to alleviate this knowledge acquisition bottleneck. We focus on two important knowledge containers: relevance knowledge, which shows relatedness of features to cases, and similarity knowledge, which captures the relatedness of features to each other. The terminology is derived from the Case Retrieval Network (CRN) retrieval architecture in TCBR, which is used as the underlying formalism in this thesis applied to text classification. Latent Semantic Indexing (LSI) generated concepts are a useful resource for relevance knowledge acquisition for CRNs. This thesis introduces a supervised LSI technique called sprinkling that exploits class knowledge to bias LSI's concept generation. An extension of this idea, called Adaptive Sprinkling has been proposed to handle inter-class relationships in complex domains like hierarchical (e.g. Yahoo directory) and ordinal (e.g. product ranking) classification tasks. Experimental evaluation results show the superiority of CRNs created with sprinkling and AS, not only over LSI on its own, but also over state-of-the-art classifiers like Support Vector Machines (SVM). Current statistical approaches based on feature co-occurrences can be utilized to mine similarity knowledge for CRNs. However, related words often do not co-occur in the same document, though they co-occur with similar words. We introduce an algorithm to efficiently mine such indirect associations, called higher order associations. Empirical results show that CRNs created with the acquired similarity knowledge outperform both LSI and SVM. Incorporating acquired knowledge into the CRN transforms it into a densely connected network. While improving retrieval effectiveness, this has the unintended effect of slowing down retrieval. We propose a novel retrieval formalism called the Fast Case Retrieval Network (FCRN) which eliminates redundant run-time computations to improve retrieval speed. Experimental results show FCRN's ability to scale up over high dimensional textual casebases. Finally, we investigate novel ways of visualizing and estimating complexity of textual casebases that can help explain performance differences across casebases. Visualization provides a qualitative insight into the casebase, while complexity is a quantitative measure that characterizes classification or retrieval hardness intrinsic to a dataset. We study correlations of experimental results from the proposed approaches against complexity measures over diverse casebases