Lexical vagueness is a ubiquitous phenomenon in natural language. Most of previous works in natural language processing (NLP) consider lexical ambiguity as the main problem in natural language understanding rather than lexical vagueness. Lexical vagueness is usually considered as a solution rather than a problem in natural language understanding since precise information is usually failed to be provided in conversations. However, lexical vagueness is obviously an obstacle in human robot interaction (HRI) since the robots are expected to precisely understand their users' utterances in order to provide reliable services to their users. This research aims to develop novel lexical vagueness handling techniques to enable service robots to precisely understand their users' utterance so that they can provide the reliable services to their users. A novel integrated system to handle lexical vagueness is proposed in this research based on an in-depth understanding of lexical ambiguity and lexical vagueness including why they exist, how they are presented, what differences are in between them, and the mainstream techniques to handle lexical ambiguity and lexical vagueness. The integrated system consists of two blocks: the block of lexical ambiguity handling and the block of lexical vagueness handling. The block of lexical ambiguity handling first removes syntactic ambiguity and lexical ambiguity. The block of lexical vagueness handling is then used to model and remove lexical vagueness. Experimental results show that the robots endowed with the developed integrated system are able to understand their users' utterances. The reliable services to their users, therefore, can be provided by the robots.

Defending models against Natural Language Processing adversarial attacks is a challenge because of the discrete nature of the text dataset. However, given the variety of Natural Language Processing applications, it is important to make text processing models more robust and secure. This paper aims to develop techniques that will help text processing models such as BERT to combat adversarial samples that contain misspellings. These developed models are more robust than off the shelf spelling checkers.

<p>In this thesis, I deal with the task of computationally processing stories with a focus on multidisciplinary ends, specifically in Digital Humanities and Cultural Analytics. In the process, I collect, clean, investigate, and predict from two datasets. The first is a dataset of 2,302 open-source literary works categorized by the time period they are set in. These works were all collected from Project Gutenberg. The classification of the time period in which the work is set was discovered by collecting and inspecting Library of Congress subject classifications, Wikipedia Categories, and literary factsheets from SparkNotes. The second is a dataset of 6,991 open-source literary works categorized by the hierarchical location the work is set in; these labels were constructed from Library of Congress subject classifications and SparkNotes factsheets. These datasets are the first of their kind and can help move forward an understanding of 1) the presentation of settings in stories and 2) the effect the settings have on our understanding of the stories.</p>

Measuring semantic similarity is a task needed in many Natural Language Processing (NLP) applications. For example, in Machine Translation evaluation, semantic similarity is used to assess the quality of the machine translation output by measuring the degree of equivalence between a reference translation and the machine translation output. The problem of semantic similarity (Corley and Mihalcea, 2005) is de ned as measuring and recognising semantic relations between two texts. Semantic similarity covers di erent types of semantic relations, mainly bidirectional and directional. This thesis proposes new methods to address the limitations of existing work on both types of semantic relations. Recognising Textual Entailment (RTE) is a directional relation where a text T entails the hypothesis H (entailment pair) if the meaning of H can be inferred from the meaning of T (Dagan and Glickman, 2005; Dagan et al., 2013). Most of the RTE methods rely on machine learning algorithms. de Marne e et al. (2006) propose a multi-stage architecture where a rst stage determines an alignment between the T-H pairs to be followed by an entailment decision stage. A limitation of such approaches is that instead of recognising a non-entailment, an alignment that ts an optimisation criterion will be returned, but the alignment by itself is a poor predictor for iii non-entailment. We propose an RTE method following a multi-stage architecture, where both stages are based on semantic representations. Furthermore, instead of using simple similarity metrics to predict the entailment decision, we use a Markov Logic Network (MLN). The MLN is based on rich relational features extracted from the output of the predicate-argument alignment structures between T-H pairs. This MLN learns to reward pairs with similar predicates and similar arguments, and penalise pairs otherwise. The proposed methods show promising results. A source of errors was found to be the alignment step, which has low coverage. However, we show that when an alignment is found, the relational features improve the nal entailment decision. The task of Semantic Textual Similarity (STS) (Agirre et al., 2012) is de- ned as measuring the degree of bidirectional semantic equivalence between a pair of texts. The STS evaluation campaigns use datasets that consist of pairs of texts from NLP tasks such as Paraphrasing and Machine Translation evaluation. Methods for STS are commonly based on computing similarity metrics between the pair of sentences, where the similarity scores are used as features to train regression algorithms. Existing methods for STS achieve high performances over certain tasks, but poor results over others, particularly on unknown (surprise) tasks. Our solution to alleviate this unbalanced performances is to model STS in the context of Multi-task Learning using Gaussian Processes (MTL-GP) ( Alvarez et al., 2012) and state-of-the-art iv STS features ( Sari c et al., 2012). We show that the MTL-GP outperforms previous work on the same datasets.

When computers generate text, they have to consider how to describe the entities mentioned in the text. This situation becomes more difficult when the audience is unknown, as it is not clear what information is available to the addressees. This thesis investigates generation of descriptions in situations when an algorithm does not have a precise model of addressee's knowledge. This thesis starts with the collection and analysis of a corpus of descriptions of famous people. The analysis of the corpus revealed a number of useful patterns, which informed the remainder of this thesis. One of the difficult questions is how to choose information that helps addressees identify the described person. This thesis introduces a corpus-based method for determining which properties are more likely to be known by the addressees, and a probability-based method to identify properties that are distinguishing. One of the patterns observed in the collected corpus is the inclusion of multiple properties each of which uniquely identifies the referent. This thesis introduces a novel corpus-based method for determining how many properties to include in a description. Finally, a number of algorithms that leverage the findings of the corpus analysis and their computational implementation are proposed and tested in an evaluation involving human participants. The proposed algorithms outperformed the Incremental Algorithm in terms of numbers of correctly identified referents and in terms of providing a better mental image of the referent. The main contributions of this thesis are: (1) a corpus-based analysis of descriptions produced for an unknown audience; (2) a computational heuristic for estimating what information is likely to be known to addressees; and (3) algorithms that can generate referring expressions that benefit addressees without having an explicit model of addressee's knowledge.

The question of whether a machine can reproduce human intelligence is older than modern computation, but has received a great deal of attention since the first digital computers emerged decades ago. Language understanding, a hallmark of human intelligence, has been the focus of a great deal of work in Artificial Intelligence (AI). In 1950, mathematician Alan Turing proposed a kind of game, or test, to evaluate the intelligence of a machine by assessing its ability to understand written natural language. But nearly sixty years after Turing proposed his test of machine intelligence—pose questions to a machine and a person without seeing either, and try to determine which is the machine—no system has passed the Turing Test, and the question of whether a machine can understand natural language cannot yet be answered. The present investigation is, firstly, an attempt to advance the state of the art in natural language understanding by building a machine whose input is English natural language and whose output is a set of assertions that represent answers to certain questions posed about the content of the input. The machine we explore here, in other words, should pass a simplified version of the Turing Test and by doing so help clarify and expand on our understanding of the machine intelligence. Toward this goal, we explore a constraint framework for partial solutions to the Turing Test, propose a problem whose solution would constitute a significant advance in natural language processing, and design and implement a system adequate for addressing the problem proposed. The fully implemented system finds primary specific events and their locations in monologue discourse using a hierarchical classification approach, and as such provides answers to questions of central importance in the interpretation of discourse. / text

This thesis explores approaches to modelling individual differences in language use. The differences under consideration fall into two broad categories: Variation of the personality projected through language, and modelling of language alignment behaviour between dialogue partners. In a way, these two aspects oppose each other – language related to varying personalities should be recognisably different, while aligning speakers agree on common language during a dialogue. The central hypothesis is that such variation can be captured and produced with restricted computational means. Results from research on personality psychology and psycholinguistics are transformed into a series of lexically-based Affective Language Production Models (ALPMs) which are parameterisable for personality and alignment. The models are then explored by varying the parameters and observing the language they generate. ALPM-1 and ALPM-2 re-generate dialogues from existing utterances which are ranked and filtered according to manually selected linguistic and psycholinguistic features that were found to be related to personality. ALPM-3 is based on true overgeneration of paraphrases from semantic representations using the OPENCCG framework for Combinatory Categorial Grammar (CCG), in combination with corpus-based ranking and filtering by way of n-gram language models. Personality effects are achieved through language models built from the language of speakers of known personality. In ALPM-4, alignment is captured via a cache language model that remembers the previous utterance and thus influences the choice of the next. This model provides a unified treatment of personality and alignment processes in dialogue. In order to evaluate the ALPMs, dialogues between computer characters were generated and presented to human judges who were asked to assess the characters’ personality. In further internal simulations, cache language models were used to reproduce results of psycholinguistic priming studies. The experiments showed that the models are capable of producing natural language dialogue which exhibits human-like personality and alignment effects.

This thesis describes the development and evaluation of new automated Question Answering (QA) methods tailored to clinical comparison questions that give clinicians a rank-ordered list of MEDLINE® abstracts targeted to natural language clinical drug comparison questions (e.g. ”Have any studies directly compared the effects of Pioglitazone and Rosiglitazone on the liver?”). Three corpora were created to develop and evaluate a new QA system for clinical comparison questions called RetroRank. RetroRank takes the clinician’s plain text question as input, processes it and outputs a rank-ordered list of potential answer candidates, i.e. MEDLINE® abstracts, that is reordered using new post-retrieval ranking strategies to ensure the most topically-relevant abstracts are displayed as high in the result set as possible. RetroRank achieves a significant improvement over the PubMed recency baseline and performs equal to or better than previous approaches to post-retrieval ranking relying on query frames and annotated data such as the approach by Demner-Fushman and Lin (2007). The performance of RetroRank shows that it is possible to successfully use natural language input and a fully automated approach to obtain answers to clinical drug comparison questions. This thesis also introduces two new evaluation corpora of clinical comparison questions with “gold standard” references that are freely available and are a valuable resource for future research in medical QA.

Each individual has a distinguished writing style. But natural language generation systems pro- duce text with much less variety. Is it possible to produce more human-like text from natural language generation systems by mimicking the style of particular authors? We start by analysing the text of real authors. We collect a corpus of texts from a single genre (food recipes) with each text identified with its author, and summarise a variety of writing features in these texts. Each author's writing style is the combination of a set of features. Analysis of the writing features shows that not only does each individual author write differently but the differences are consistent over the whole of their corpus. Hence we conclude that authors do keep consistent style consisting of a variety of different features. When we discuss notions such as the style and meaning of texts, we are referring to the reac- tion that readers have to them. It is important, therefore, in the field of computational linguistics to experiment by showing texts to people and assessing their interpretation of the texts. In our research we move the thesis from simple discussion and statistical analysis of the properties of text and NLG systems, to perform experiments to verify the actual impact that lexical preference has on real readers. Through experiments that require participants to follow a recipe and prepare food, we conclude that it is possible to alter the lexicon of a recipe without altering the actions performed by the cook, hence that word choice is an aspect of style rather than semantics; and also that word choice is one of the writing features employed by readers in identifying the author of a text. Among all writing features, individual lexical preference is very important both for analysing and generating texts. So we choose individual lexical choice as our principal topic of research. Using a modified version of distributional similarity CDS) helps us to choose words used by in- dividual authors without the limitation of many other solutions such as a pre-built thesauri. We present an algorithm for analysis and rewriting, and assess the results. Based on the results we propose some further improvements.

The next generation of the web, the Semantic Web, integrates distributed web resources from various domains by allowing data (instantial and ontological data) to be shared and reused across applications, enterprise and community boundaries based on the Resource Description Framework (RDF). Nevertheless, the RDF was not developed for casual users who are unfamiliar with the RDF but interested in data represented using RDF. NLG may be a possible solution to bridging the gap between the casual users and RDF data, but the cost of separately applying fine grained NLG techniques for every domain in the Semantic Web would be extremely high, and hence not realistic.