Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation

Word sense disambiguation (WSD)—the task of determining which meaning a word carries in a particular context—is a core research problem in computational linguistics. Though it has long been recognized that supervised (machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs). These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. This dissertation is concerned with extending the efficacy and applicability of knowledge-based word sense disambiguation. First, we investigate two approaches for bridging the information gap and thereby improving the performance of knowledge-based WSD. In the first approach we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. Our next main contribution is to adapt techniques from word sense disambiguation to a novel task: the interpretation of puns. Traditional NLP applications, including WSD, usually treat the source text as carrying a single meaning, and therefore cannot cope with the intentionally ambiguous constructions found in humour and wordplay. We describe how algorithms and evaluation methodologies from traditional word sense disambiguation can be adapted for the "disambiguation" of puns, or rather for the identification of their double meanings. Finally, we cover the design and construction of technological and linguistic resources aimed at supporting the research and application of word sense disambiguation. Development and comparison of WSD systems has long been hampered by a lack of standardized data formats, language resources, software components, and workflows. To address this issue, we designed and implemented a modular, extensible framework for WSD. It implements, encapsulates, and aggregates reusable, interoperable components using UIMA, an industry-standard information processing architecture. We have also produced two large sense-annotated data sets for under-resourced languages or domains: one of these targets German-language text, and the other English-language puns

Semantic Tagging for the Urdu Language:Annotated Corpus and Multi-Target Classification Methods

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as natural language processing, corpus linguistics, information retrieval, and data science. An important aspect of such automatic information extraction and analysis is the annotation of language data using semantic tagging tools. Different semantic tagging tools have been designed to carry out various levels of semantic analysis, for instance, named entity recognition and disambiguation, sentiment analysis, word sense disambiguation, content analysis, and semantic role labelling. Common to all of these tasks, in the supervised setting, is the requirement for a manually semantically annotated corpus, which acts as a knowledge base from which to train and test potential word and phrase-level sense annotations. Many benchmark corpora have been developed for various semantic tagging tasks, but most are for English and other European languages. There is a dearth of semantically annotated corpora for the Urdu language, which is widely spoken and used around the world. To fill this gap, this study presents a large benchmark corpus and methods for the semantic tagging task for the Urdu language. The proposed corpus contains 8,000 tokens in the following domains or genres: news, social media, Wikipedia, and historical text (each domain having 2K tokens). The corpus has been manually annotated with 21 major semantic fields and 232 sub-fields with the USAS (UCREL Semantic Analysis System) semantic taxonomy which provides a comprehensive set of semantic fields for coarse-grained annotation. Each word in our proposed corpus has been annotated with at least one and up to nine semantic field tags to provide a detailed semantic analysis of the language data, which allowed us to treat the problem of semantic tagging as a supervised multi-target classification task. To demonstrate how our proposed corpus can be used for the development and evaluation of Urdu semantic tagging methods, we extracted local, topical and semantic features from the proposed corpus and applied seven different supervised multi-target classifiers to them. Results show an accuracy of 94% on our proposed corpus which is free and publicly available to download

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus

WebCAGe - a web-harvested corpus annotated with GermaNet senses

This paper describes an automatic method for creating a domain-independent senseannotated corpus harvested from the web. As a proof of concept, this method has been applied to German, a language for which sense-annotated corpora are still in short supply. The sense inventory is taken from the German wordnet GermaNet. The web-harvesting relies on an existing mapping of GermaNet to the German version of the web-based dictionary Wiktionary. The data obtained by this method constitut