Information Retrieval by Semantically Grouping Search Query Data
by Wim Florijn
Query data analysis is a time-consuming task. Currently, a method exists where word (combinations) in queries are labelled by using an information collection consisting of regular expressions. Because the information collection does not contain regular expressions from never-before seen domains, the method heavily relies on manual work, resulting in decreased scalibility. Therefore, a machine-learning based method is proposed in order to automate the annotation of word (combinations) in queries. This research searches for the optimal configuration of a pre-processing method, word embedding model, additional data set and classifier variant. All configurations have been examined on multiple data sets, and appropriate performance metrics have been calculated. The results show that the optimal configuration consists of omitting pre-processing, training a fastText model and enriching word features using additional data in combination with a recurrent classifier. We found that an approach using machine learning is able to obtain excellent performance on the task of labelling word (combinations) in search queries.
Logical Structure Extraction of Electronic Documents Using Contextual Information
by Semere Bitew
Logical document structure extraction refers to the process of coupling the semantic meanings (logical labels) such as title, authors, affiliation, etc., to physical sections in a document. For example, in scientific papers the first paragraph is usually a title. Logical document structure extraction is a challenging natural language processing problem. Elsevier, as one of the biggest scientific publishers in the world, is working on recovering logical structure from article submissions in its project called the Apollo project. The current process in this project requires the involvement of human annotators to make sure logical entities in articles are labelled with correct tags, such as title, abstract, heading, reference-item and so on. This process can be more efficient in producing correct tags and in providing high quality and consistent publishable article papers if it is automated. A lot of research has been done to automatically extract the logical structure of documents. In this thesis, a document is defined as a sequence of paragraphs and recovering the labels for each paragraph yields the logical structure of a document. For this purpose, we proposed a novel approach that combines random forests with conditional random fields (RF-CRFs) and long short-term memory with CRFs (LSTM-CRFs). Two variants of CRFs called linear-chain CRFs (LCRFs) and dynamic CRFs (DCRFs) are used in both of the proposed approaches. These approaches consider the label information of surrounding paragraphs when classifying paragraphs. Three categories of features namely, textual, linguistic and markup features are extracted to build the RF-CRF models. A word embedding is used as an input to build the LSTM-CRF models. Our models were evaluated for extracting reference-items on Elsevier’s Apollo dataset of 146,333 paragraphs. Our results show that LSTM-CRF models trained on the dataset outperform the RF-CRF models and existing approaches. We show that the LSTM component efficiently uses past feature inputs within a paragraph. The CRF component is able to exploit the contextual information using the tag information of surrounding paragraphs. It was observed that the feature categories are complementary. They produce the best performance when all the features are used. On the other hand, this manual feature extraction can be replaced with an LSTM, where no handcrafted features are used, achieving a better performance. Additionally, the inclusion of features generated for the previous and next paragraph as part of the feature vector for classifying the current paragraph improved the performance of all the models.
by Jordy Michorius.
In this research an approach for bias reduction, while still maintaining usability of the classifier, is proposed. The approach for bias reduction requires all preprocessing to be done, include one-hot encoding and making the training and test set split. The approach then requires a banned feature, a feature that has for example been deemed morally irrelevant for the classification purpose. For the bias reduction, the proposal is to use the KS-score obtained from the two sample KS-test to determine how well a feature contributes to classification and how well it contributes to the bias of the banned feature. So that means that all features present in the dataset that are not the label(L) or the banned feature(B), that the following holds for feature X to be safe to use in the training dataset:
KS–score(X|L=1, X|L=0) > KS–score(X|B=1, X|B=0)
After all features are checked, the unsafe (or flagged) features need to be removed from both the training and the test set in order to make the classifier as fair as possible. The datasets that have been used are the Titanic dataset, with as banned feature the passenger class and a Financial survey, with as banned feature the race. The results have shown that the overall bias has been reduced for both the Titanic dataset and the Financial survey. However in terms of relative fairness, the Financial survey is the only one that became less fair for a certain banned feature value (Race = White). All other values became fairer for both the Financial survey and the Titanic dataset.
Automatic Product Name Recognition from Short Product Descriptions
by Elnaz Pazhouhi
This thesis studies the problem of product name recognition from short product descriptions. This is an important problem especially with the increasing use of ERP (Enterprise Resource Planning) software at the core of modern business management systems, where the information of business transactions is stored in unstructured data stores. A solution to the problem of product name recognition is especially useful for the intermediate businesses as they are interested in finding potential matches between the items in product catalogs (produced by manufactures or another intermediate business) and items in the product requests (given by the end user or another intermediate business).
In this context the problem of product name recognition in specifically challenging because product descriptions are typically short, ungrammatical, incomplete, abbreviated and multilingual. In this thesis we investigate the application of supervised machine-learning techniques and gazetteer-based techniques to our problem. To approach the problem, we define it as a classification problem where the tokens of product descriptions are classified into I, O and B classes according to the standard IOB tagging scheme. Next we investigate and compare the performance of a set of hybrid solutions that combine machine learning and gazetteer-based approaches. We study a solution space that uses four learning models: linear and non-linear SVC, Random Forest, and AdaBoost. For each solution, we use the same set of features. We divide the features into four categories: token-level features, document-level features, gazetteer-based features and frequency-based features. Moreover, we use automatic feature selection to reduce the dimensionality of data; that consequently improves the training efficiency and avoids over-fitting.
To be able to evaluate the solutions, we develop a machine learning framework that takes as its inputs a list of predefined solutions (i.e. our solution space) and a preprocessed labeled dataset (i.e. a feature vector X, and a corresponding class label vector Y). It automatically selects the optimal number of most relevant features, optimizes the hyper-parameters of the learning models, trains the learning models, and evaluates the solution set. We believe that our automated machine learning framework, can effectively be used as an AutoML framework that automates most of the decisions that have to be made in the design process of a machine learning solution for a particular domain (e.g. for product name recognition).
Moreover, we conduct a set of experiments and based on the results, we answer the research questions of this thesis. In particular, we determine (1) which learning models are more effective for our task, (2) which feature groups contain the most relevant features (3) what is the contribution of different feature groups to the overall performance of the induced model, (4) how gazetteer-based features are incorporated with the machine learning solutions, (5) how effective gazetteer-based features are, (6) what the role of hyper-parameter optimization is and (7) which models are more sensitive to the hyper-parameters optimization.
According to our results, the solutions with maximum and minimum performance are non-linear SVC with an F1 measure of 65% and AdaBoost with an F1 measure of 59% respectively. This reveals that the role of classifiers is not considerable in the final outcome of the learning model, at least according to the studied dataset. Additionally, our results show that the most effective feature group is the document-level features with 14.8% contribution to the overall performance (i.e. F1 measure), in the second position, there is the group of token-level features, with 6.8% contribution. The other two groups, the gazetteer-based features and frequency-based features have small contributions of 1% and 0.5% respectively. However more investigations relate the poor performance of gazetteer-based features to the low coverage of the used gazetteer (i.e. ETIM).
Our experiments also show that all learning models over-fit the training data when a large number of features is used; thus the use of feature selection techniques is essential to the robustness of the proposed solutions. Among the studied learning models, the performance of non-linear SVC and AdaBoost models strongly depends on the used hyper-parameters. Therefore for those models the computational cost of the hyper-parameters tuning is justifiable.
Slides of the keynote at the 1st International Workshop on LEARning Next gEneration Rankers, LEARNER 2017 on 1 October 2017 in Amsterdam are now available:
Download the paper: Niek Tax, Sander Bockting, and Djoerd Hiemstra. “A cross-benchmark comparison of 87 learning to rank methods'’, Information Processing and Management 51(6), Elsevier, pages 757–772, 2015 [download pdf]
by Fieke Hillerström
Deep learning for biometrics has increasingly gained attention over the last years. Due to the expansion of computational power and the increasing sizes of the available datasets, the performance has surpassed that of humans on certain verification tasks. However, large datasets are not available for every application. Therefore we introduce Deep Verification Learning, to reduce network complexity and train with more modest hardware on smaller datasets. Deep Verification Learning takes two images to be verified at the input of a deep learning network, and trains directly towards a verification score. This topology enables the network to learn differences and similarities in the first layer, and to involve verification signals during training. Directly training towards a verification score reduces the number of trainable parameters significantly. We applied Deep Verification Learning on the face verification task, also it could be extended to other biometric modalities. We compared our face verification learning topology with a network trained for multi-class classification on the FRGC dataset, which contains only 568 subjects. Deep Verification Learning performs substantially better.
We are proud of the Information Processing & Management Best Paper Award 2015 for our paper: A cross-benchmark comparison of 87 learning to rank methods.
Published in Information Processing and Management 51(6), pages 757–772
Machine Learning Research at the University of Twente focusses on the application of Machine Learning in Social Signal Processing, Biometric Pattern Recognition, and Text mining. Have a look at our new web site at: http://ml.ewi.utwente.nl.
Co-occurrence Rate Networks: Towards separate training for undirected graphical models
by Zhemin Zhu
Dependence is a universal phenomenon which can be observed everywhere. In machine learning, probabilistic graphical models (PGMs) represent dependence relations with graphs. PGMs find wide applications in natural language processing (NLP), speech processing, computer vision, biomedicine, information retrieval, etc. Many traditional models, such as hidden Markov models (HMMs), Kalman filters, can be put under the umbrella of PGMs. The central idea of PGMs is to decompose (factorize) a joint probability into a product of local factors. Learning, inference and storage can be conducted efficiently over the factorization representation.
Two major types of PGMs can be distinguished: (i) Bayesian networks (directed graphs), and (ii) Markov networks (undirected graphs). Bayesian networks represent directed dependence with directed edges. Local factors of Bayesian networks are conditional probabilities. Directed dependence, directed edges and conditional probabilities are all asymmetric notions. In contrast, Markov networks represent mutual dependence with undirected edges. Both of mutual dependence and undirected edges are symmetric notions. For general Markov networks, based on the Hammersley–Clifford theorem, local factors are positive functions over maximum cliques. These local factors are explained using intuitive notions like ‘compatibility’ or ‘affinity’. Specially, if a graph forms a clique tree, the joint probability can be reparameterized into a junction tree factorization.
In this thesis, we propose a novel framework motivated by the Minimum Shared Information Principle (MSIP): We try to find a factorization in which the information shared between factors is minimum. In other words, we try to make factors as independent as possible.
The benefit of doing this is that we can train factors separately without paying a lot of efforts to guarantee consistency between them. To achieve this goal, we develop a theoretical framework called co-occurrence rate networks (CRNs) to obtain such a factorization. Briefly, given a joint probability, the CRN factorization is obtained as follows. We first strip off singleton probabilities from the joint probability. The quantity left is called co-occurrence rate (CR). CR is a symmetric quantity which measures mutual dependence among variables involved. Then we further decompose the joint CR into smaller and indepen dent CRs. Finally, we obtain a CRN factorization whose factors consist of all singleton probabilities and CR factors. There exist two kinds of independencies between these factors: (i) a singleton probability is independent (Here independent means two factors do not share information.) of other singleton probabilities; (ii) a CR factor is independent of other CR factors conditioned by singleton probabilities. Based on a CRN factorization, we propose an efficient two-step separate training method: (i) in the first step, we train a separate model for each singleton probability; (ii) given singleton probabilities, we train a separate model for each CR factor. Experimental results on three important natural language processing tasks show that our separate training method is two orders of magnitude faster than conditional random fields, while achieving competitive quality (often better on the overall quality metric F1).
The second contribution of this thesis is applying PGMs to a real-world NLP application: open relation extraction (ORE). In open relation extraction, two entities in a sentence are given, and the goal is to automatically extract their relation expression. ORE is a core technique, especially in the age of big data, for transforming unstructured information into structured data. We propose our model SimpleIE for this task. The basic idea is to decompose an extraction pattern into a sequence of simplification operations (components). The benefit by doing this is that these components can be re-combined in a new way to generate new extraction patterns. Hence SimpleIE can represent and capture diverse extraction patterns. This model is essentially a sequence labeling model. Experimental results on three benchmark data sets show that SimpleIE boosts recall and F1 by at least 15% comparing with seven ORE systems.
As tangible outputs of this thesis, we contribute open source implementations of our research results as well as an annotated data set.
by Niek Tax (Eindhoven University), Sander Bockting (Avanade), and Djoerd Hiemstra
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered by the absence of a standard set of evaluation benchmark collections. In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets. Our comparison methodology consists of two components: (1) Normalized Winning Number, which gives insight in the ranking accuracy of the learning to rank method, and (2) Ideal Winning Number, which gives insight in the degree of certainty concerning its ranking accuracy. Evaluation results of 87 learning to rank methods on 20 well-known benchmark datasets are collected through a structured literature search. ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning to rank methods in the Normalized Winning Number and Ideal Winning Number dimensions, listed in increasing order of Normalized Winning Number and decreasing order of Ideal Winning Number.
To appear in November in Information Processing and Management 51(6), pages 757–772