Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semi-supervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semisupervised algorithms on both vector-based and graph-based data sets.
ML ID: 224
This paper introduces a new kernel which computes similarity between two natural language sentences as the number of paths shared by their dependency trees. The paper gives a very efficient algorithm to compute it. This kernel is also an improvement over the word subsequence kernel because it only counts linguistically meaningful word subsequences which are based on word dependencies. It overcomes some of the difficulties encountered by syntactic tree kernels as well. Experimental results demonstrate the advantage of this kernel over word subsequence and syntactic tree kernels.
ML ID: 223
Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing systems that understand natural language input. This thesis presents a new machine learning approach for semantic parsing based on string-kernel-based classification. It takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classifiers. This method does not use any hard-matching rules and unlike previous and other recent methods, does not use grammar rules for natural language, probabilistic or otherwise, which makes it more robust to noisy input.Besides being robust, this approach is also flexible and able to learn under a wide range of supervision, from extra to weaker forms of supervision. It can easily utilize extra supervision given in the form of syntactic parse trees for natural language sentences by using a syntactic tree kernel instead of a string kernel. Its learning algorithm can also take advantage of detailed supervision provided in the form of semantically augmented parse trees. A simple extension using transductive SVMs enables the system to do semi-supervised learning and improve its performance utilizing unannotated sentences which are usually easily available. Another extension involving EM-like retraining makes the system capable of learning under ambiguous supervision in which the correct meaning representation for each sentence is not explicitly given, but instead a set of possible meaning representations is given. This weaker and more general form of supervision is better representative of a natural training environment for a language-learning system requiring minimal human supervision.
For a semantic parser to work well, conformity between natural language and meaning representation grammar is necessary. However meaning representation grammars are typically designed to best suit the application which will use the meaning representations with little consideration for how well they correspond to natural language semantics. We present approaches to automatically transform meaning representation grammars to make them more compatible with natural language semantics and hence more suitable for learning semantic parsers. Finally, we also show that ensembles of different semantic parser learning systems can obtain the best overall performance.
ML ID: 215
Information Extraction, the task of locating textual mentions of specific types of entities and their relationships, aims at representing the information contained in text documents in a structured format that is more amenable to applications in data mining, question answering, or the semantic web. The goal of our research is to design information extraction models that obtain improved performance by exploiting types of evidence that have not been explored in previous approaches. Since designing an extraction system through introspection by a domain expert is a laborious and time consuming process, the focus of this thesis will be on methods that automatically induce an extraction model by training on a dataset of manually labeled examples.Named Entity Recognition is an information extraction task that is concerned with finding textual mentions of entities that belong to a predefined set of categories. We approach this task as a phrase classification problem, in which candidate phrases from the same document are collectively classified. Global correlations between candidate entities are captured in a model built using the expressive framework of Relational Markov Networks. Additionally, we propose a novel tractable approach to phrase classification for named entity recognition based on a special Junction Tree representation.
Classifying entity mentions into a predefined set of categories achieves only a partial disambiguation of the names. This is further refined in the task of Named Entity Disambiguation, where names need to be linked to their actual denotations. In our research, we use Wikipedia as a repository of named entities and propose a ranking approach to disambiguation that exploits learned correlations between words from the name context and categories from the Wikipedia taxonomy.
Relation Extraction refers to finding relevant relationships between entities mentioned in text documents. Our approaches to this information extraction task differ in the type and the amount of supervision required. We first propose two relation extraction methods that are trained on documents in which sentences are manually annotated for the required relationships. In the first method, the extraction patterns correspond to sequences of words and word classes anchored at two entity names occurring in the same sentence. These are used as implicit features in a generalized subsequence kernel, with weights computed through training of Support Vector Machines. In the second approach, the implicit extraction features are focused on the shortest path between the two entities in the word-word dependency graph of the sentence. Finally, in a significant departure from previous learning approaches to relation extraction, we propose reducing the amount of required supervision to only a handful of pairs of entities known to exhibit or not exhibit the desired relationship. Each pair is associated with a bag of sentences extracted automatically from a very large corpus. We extend the subsequence kernel to handle this weaker form of supervision, and describe a method for weighting features in order to focus on those correlated with the target relation rather than with the individual entities. The resulting Multiple Instance Learning approach offers a competitive alternative to previous relation extraction methods, at a significantly reduced cost in human supervision.
ML ID: 213
We present a new approach for mapping natural language sentences to their formal meaning representations using string-kernel-based classifiers. Our system learns these classifiers for every production in the formal language grammar. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these string classifiers. Our experiments on two real-world data sets show that this approach compares favorably to other existing systems and is particularly robust to noise.
ML ID: 191
We present a new method for detecting and disambiguating named entities in open domain text. A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia. The resulting model significantly outperforms a less informed baseline.
ML ID: 185
We present a new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels. This kernel uses three types of subsequence patterns that are typically employed in natural language to assert relationships between two entities. Experiments on extracting protein interactions from biomedical corpora and top-level relations from newspaper corpora demonstrate the advantages of this approach.
ML ID: 169
Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing user-friendly natural language interfaces to computing systems. Most of the research in natural language understanding, however, has mainly focused on shallow semantic analysis like case-role analysis or word sense disambiguation. The existing work in semantic parsing either lack the robustness of statistical methods or are applicable only to simple domains where semantic analysis is equivalent to filling a single semantic frame.
In this proposal, we present a new approach to semantic parsing based on string-kernel-based classification. Our system takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Each classifier then gives the probability of the production covering any given natural language string of words. These classifiers are further refined using EM-type iterations based on their performance on the training data. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classifiers. Our experiments on two real-world data sets that have deep meaning representations show that this approach compares favorably to other existing systems in terms of accuracy and coverage.
For future work, we propose to extend this approach so that it will also exploit the knowledge of natural language syntax by using the existing syntactic parsers. We also intend to broaden the scope of application domains, for example, domains where the sentences are noisy as typical in speech, or domains where corpora available for training do not have natural language sentences aligned with their unique meaning representations. We aim to test our system on the task of complex relation extraction as well. Finally, we also plan to investigate ways to combine our semantic parser with some recently developed semantic parsers to form committees in order to get the best overall performance.
ML ID: 181
We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
ML ID: 175
Clustering is one of the most common data mining tasks, used frequently for data categorization and analysis in both industry and academia. The focus of our research is on semi-supervised clustering, where we study how prior knowledge, gathered either from automated information sources or human supervision, can be incorporated into clustering algorithms. In this thesis, we present probabilistic models for semi-supervised clustering, develop algorithms based on these models and empirically validate their performances by extensive experiments on data sets from different domains, e.g., text analysis, hand-written character recognition, and bioinformatics.
In many domains where clustering is applied, some prior knowledge is available either in the form of labeled data (specifying the category to which an instance belongs) or pairwise constraints on some of the instances (specifying whether two instances should be in same or different clusters). In this thesis, we first analyze effective methods of incorporating labeled supervision into prototype-based clustering algorithms, and propose two variants of the well-known KMeans algorithm that can improve their performance with limited labeled data.
We then focus on the problem of semi-supervised clustering with constraints and show how this problem can be studied in the framework of a well-defined probabilistic generative model of a Hidden Markov Random Field. We derive an efficient KMeans-type iterative algorithm, HMRF-KMeans, for optimizing a semi-supervised clustering objective function defined on the HMRF model. We also give convergence guarantees of our algorithm for a large class of clustering distortion measures (e.g., squared Euclidean distance, KL divergence, and cosine distance).
Finally, we develop an active learning algorithm for acquiring maximally informative pairwise constraints in an interactive query-driven framework, which to our knowledge is the first active learning algorithm for semi-supervised clustering with constraints.
Other interesting problems of semi-supervised clustering that we discuss in this thesis include (1) semi-supervised graph-based clustering using kernels, (2) using prior knowledge to improve overlapping clustering of data, (3) integration of both constraint-based and distance-based semi-supervised clustering methods using the HMRF model, and (4) model selection techniques that use the available supervision to automatically select the right number of clusters.
ML ID: 174
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most of the semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective. A recent theoretical connection between kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.
ML ID: 167
This paper presents the results of a large-scale effort to construct a comprehensive database of known human protein interactions by combining and linking known interactions from existing databases and then adding to them by automatically mining additional interactions from 750,000 Medline abstracts. The end result is a network of 31,609 interactions amongst 7,748 proteins. The text mining system first identifies protein names in the text using a trained Conditional Random Field (CRF) and then identifies interactions through a filtered co-citation analysis. We also report two new strategies for mining interactions, either by finding explicit statements of interactions in the text using learned pattern-based rules or a Support-Vector Machine using a string kernel. Using information in existing ontologies, the automatically extracted data is shown to be of equivalent accuracy to manually curated data sets.
ML ID: 164
