Challenges in Short Text Classification: The Case of Online Auction Disclosure

Text classification is an important research problem in many fields. We examine a special case of textual content namely, short text. Examples of short text appear in a number of contexts such as online reviews, chat messages, twitter feeds, etc. In this research, we examine short text for the purpose of classification in internet auctions. The “ask seller a question” forum of a large horizontal intermediary auction platform is used to conduct this research. We describe our approach to classification by examining various solution methods to the problem. The unsupervised K-Medoids clustering algorithm provides useful but limited insights into keywords extraction while the supervised Naïve Bayes algorithm successfully achieves on average, around 65% classification accuracy. We then present a score assigning approach to this issue which outperforms the other two methods. Finally, we discuss how our approach to short text classification can be used to analyse the effectiveness of internet auctions

Applying Wikipedia to Interactive Information Retrieval

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval

Terminologies, Lexical Hierarchies and other Configurations

The focus of the monograph is on hierarchical systems of lexical items, particularly in scientific terminologies. It includes research outcomes from the dissertation Lexical Hierarchies in the Scientific Terminology, supplemented with a broad introduction to the typology of lexical and semantic relations and to a variety of branching and non-branching hierarchies, proportional series and other types of lexical configurations. It analyses the principles of formation of terminological classificatory hierarchies and identifies sense relations between items at superordinate and subordinate levels, and those at the same level. Specific morphological and onomatological properties of different languages influence consistency of corresponding lexical hierarchies, altghough the conceptual systems are identical

Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web

Constructing a biodiversity terminological inventory.

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications

Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP

On link predictions in complex networks with an application to ontologies and semantics

It is assumed that ontologies can be represented and treated as networks and that these networks show properties of so-called complex networks. Just like ontologies “our current pictures of many networks are substantially incomplete” (Clauset et al., 2008, p. 3ff.). For this reason, networks have been analyzed and methods for identifying missing edges have been proposed. The goal of this thesis is to show how treating and understanding an ontology as a network can be used to extend and improve existing ontologies, and how measures from graph theory and techniques developed in social network analysis and other complex networks in recent years can be applied to semantic networks in the form of ontologies. Given a large enough amount of data, here data organized according to an ontology, and the relations defined in the ontology, the goal is to find patterns that help reveal implicitly given information in an ontology. The approach does not, unlike reasoning and methods of inference, rely on predefined patterns of relations, but it is meant to identify patterns of relations or of other structural information taken from the ontology graph, to calculate probabilities of yet unknown relations between entities. The methods adopted from network theory and social sciences presented in this thesis are expected to reduce the work and time necessary to build an ontology considerably by automating it. They are believed to be applicable to any ontology and can be used in either supervised or unsupervised fashion to automatically identify missing relations, add new information, and thereby enlarge the data set and increase the information explicitly available in an ontology. As seen in the IBM Watson example, different knowledge bases are applied in NLP tasks. An ontology like WordNet contains lexical and semantic knowl- edge on lexemes while general knowledge ontologies like Freebase and DBpedia contain information on entities of the non-linguistic world. In this thesis, examples from both kinds of ontologies are used: WordNet and DBpedia. WordNet is a manually crafted resource that establishes a network of representations of word senses, connected to the word forms used to express these, and connect these senses and forms with lexical and semantic relations in a machine-readable form. As will be shown, although a lot of work has been put into WordNet, it can still be improved. While it already contains many lexical and semantical relations, it is not possible to distinguish between polysemous and homonymous words. As will be explained later, this can be useful for NLP problems regarding word sense disambiguation and hence QA. Using graph- and network-based centrality and path measures, the goal is to train a machine learning model that is able to identify new, missing relations in the ontology and assign this new relation to the whole data set (i.e., WordNet). The approach presented here will be based on a deep analysis of the ontology and the network structure it exposes. Using different measures from graph theory as features and a set of manually created examples, a so-called training set, a supervised machine learning approach will be presented and evaluated that will show what the benefit of interpreting an ontology as a network is compared to other approaches that do not take the network structure into account. DBpedia is an ontology derived from Wikipedia. The structured information given in Wikipedia infoboxes is parsed and relations according to an underlying ontology are extracted. Unlike Wikipedia, it only contains the small amount of structured information (e.g., the infoboxes of each page) and not the large amount of unstructured information (i.e., the free text) of Wikipedia pages. Hence DBpedia is missing a large number of possible relations that are described in Wikipedia. Also compared to Freebase, an ontology used and maintained by Google, DBpedia is quite incomplete. This, and the fact that Wikipedia is expected to be usable to compare possible results to, makes DBpedia a good subject of investigation. The approach used to extend DBpedia presented in this thesis will be based on a thorough analysis of the network structure and the assumed evolution of the network, which will point to the locations of the network where information is most likely to be missing. Since the structure of the ontology and the resulting network is assumed to reveal patterns that are connected to certain relations defined in the ontology, these patterns can be used to identify what kind of relation is missing between two entities of the ontology. This will be done using unsupervised methods from the field of data mining and machine learning