Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

Wide-coverage parsing for Turkish

Wide-coverage parsing is an area that attracts much attention in natural language processing research. This is due to the fact that it is the first step tomany other applications in natural language understanding, such as question answering. Supervised learning using human-labelled data is currently the best performing method. Therefore, there is great demand for annotated data. However, human annotation is very expensive and always, the amount of annotated data is much less than is needed to train well-performing parsers. This is the motivation behind making the best use of data available. Turkish presents a challenge both because syntactically annotated Turkish data is relatively small and Turkish is highly agglutinative, hence unusually sparse at the whole word level. METU-Sabancı Treebank is a dependency treebank of 5620 sentences with surface dependency relations and morphological analyses for words. We show that including even the crudest forms of morphological information extracted from the data boosts the performance of both generative and discriminative parsers, contrary to received opinion concerning English. We induce word-based and morpheme-based CCG grammars from Turkish dependency treebank. We use these grammars to train a state-of-the-art CCG parser that predicts long-distance dependencies in addition to the ones that other parsers are capable of predicting. We also use the correct CCG categories as simple features in a graph-based dependency parser and show that this improves the parsing results. We show that a morpheme-based CCG lexicon for Turkish is able to solve many problems such as conflicts of semantic scope, recovering long-range dependencies, and obtaining smoother statistics from the models. CCG handles linguistic phenomena i.e. local and long-range dependencies more naturally and effectively than other linguistic theories while potentially supporting semantic interpretation in parallel. Using morphological information and a morpheme-cluster based lexicon improve the performance both quantitatively and qualitatively for Turkish. We also provide an improved version of the treebank which will be released by kind permission of METU and Sabancı

Person-based Prominence in Ojibwe

This dissertation develops a formal and psycholinguistic theory of person-based prominence effects, the finding that certain categories of person such as first and second (the local persons) are privileged by the grammar. The thesis takes on three questions: (i) What are the possible categories related to person? (ii) What are the possible prominence relationships between these categories? And (iii) how is prominence information used to parse and interpret linguistic input in real time? The empirical through-line is understanding obviation — a “spotlighting” system, found most prominently in the Algonquian family of languages, that splits the (ani- mate) third persons into two categories: proximate, the person who is in the spotlight, and obviative, the persons who are introduced into the discourse, but are not in the spotlight. I provide a semantics for the feature [proximate], and detail a lattice-based theory of feature composition to derive the categories related to obviation in Border Lakes Ojibwe and beyond. This leads to insights about the syntactic and semantic relationships between person, animacy-based noun classification, number, and obviation. The novel contribution to the theory of person-based prominence effects is to de- compose person features into sets of primitives. This proposal allows the stipulated entailment relationships between categories and features, as encoded in prominence hierarchies and feature geometries, to be derived from the first principles of set theory. I further motivate the account by showing that it has increased empirical coverage, and apply it to capture patterns of agreement and word order in Border Lakes Ojibwe. Finally, I present a psycholinguistic study on how obviation is used to process filler- gap dependencies in Border Lakes Ojibwe. I show that obviation, and by extension, prominence information more generally, is used immediately to predictively encode movement chains, prior to bottom-up information from voice marking about the argument structure of the clause. I argue for a modular and syntax-first model of parsing, revising the Active Filler Strategy to be guided by pressures to minimize syntactic distance and maximize the expected well-formedness of each link in the chain. These pressures compete, accounting for effects of prediction, integration, and reanalysis in long-distance dependency formation

Constraints on Language Learning : behavioral and neurocognitive studies with adults and children

This thesis will contribute to a body of experimental work addressing the question of whether language learning plays a role in certain fundamental design properties of natural languages. Methodologically, this thesis seeks to extend the artificial language learning paradigm, investigating whether learners are sensitive to the constraints embodied by key properties of languages. For example, we will explore whether communicative pressure influences the final outcome of language learning, namely how the structures that are acquired by individuals are transmitted to downstream generations. We will also explore how basic language learning constraints operate in different age groups and, importantly, cross-linguistically. Next to the behavioral experiments focusing on learning and its outcomes, we will look at preliminary electrophysiological correlates of basic compositional processing in the early stages of learning a miniature artificial language using electroencephalography (EEG). In this general introduction I will briefly discuss some of the relevant concepts and methods which will be used in three studies that constitute this thesis

Towards More Human-Like Text Summarization: Story Abstraction Using Discourse Structure and Semantic Information.

PhD ThesisWith the massive amount of textual data being produced every day, the ability to effectively summarise text documents is becoming increasingly important. Automatic text summarization entails the selection and generalisation of the most salient points of a text in order to produce a summary. Approaches to automatic text summarization can fall into one of two categories: abstractive or extractive approaches. Extractive approaches involve the selection and concatenation of spans of text from a given document. Research in automatic text summarization began with extractive approaches, scoring and selecting sentences based on the frequency and proximity of words. In contrast, abstractive approaches are based on a process of interpretation, semantic representation, and generalisation. This is closer to the processes that psycholinguistics tells us that humans perform when reading, remembering and summarizing. However in the sixty years since its inception, the field has largely remained focused on extractive approaches. This thesis aims to answer the following questions. Does knowledge about the discourse structure of a text aid the recognition of summary-worthy content? If so, which specific aspects of discourse structure provide the greatest benefit? Can this structural information be used to produce abstractive summaries, and are these more informative than extractive summaries? To thoroughly examine these questions, they are each considered in isolation, and as a whole, on the basis of both manual and automatic annotations of texts. Manual annotations facilitate an investigation into the upper bounds of what can be achieved by the approach described in this thesis. Results based on automatic annotations show how this same approach is impacted by the current performance of imperfect preprocessing steps, and indicate its feasibility. Extractive approaches to summarization are intrinsically limited by the surface text of the input document, in terms of both content selection and summary generation. Beginning with a motivation for moving away from these commonly used methods of producing summaries, I set out my methodology for a more human-like approach to automatic summarization which examines the benefits of using discourse-structural information. The potential benefit of this is twofold: moving away from a reliance on the wording of a text in order to detect important content, and generating concise summaries that are independent of the input text. The importance of discourse structure to signal key textual material has previously been recognised, however it has seen little applied use in the field of autovii matic summarization. A consideration of evaluation metrics also features significantly in the proposed methodology. These play a role in both preprocessing steps and in the evaluation of the final summary product. I provide evidence which indicates a disparity between the performance of coreference resolution systems as indicated by their standard evaluation metrics, and their performance in extrinsic tasks. Additionally, I point out a range of problems for the most commonly used metric, ROUGE, and suggest that at present summary evaluation should not be automated. To illustrate the general solutions proposed to the questions raised in this thesis, I use Russian Folk Tales as an example domain. This genre of text has been studied in depth and, most importantly, it has a rich narrative structure that has been recorded in detail. The rules of this formalism are suitable for the narrative structure reasoning system presented as part of this thesis. The specific discourse-structural elements considered cover the narrative structure of a text, coreference information, and the story-roles fulfilled by different characters. The proposed narrative structure reasoning system produces highlevel interpretations of a text according to the rules of a given formalism. For the example domain of Russian Folktales, a system is implemented which constructs such interpretations of a tale according to an existing set of rules and restrictions. I discuss how this process of detecting narrative structure can be transferred to other genres, and a key factor in the success of this process: how constrained are the rules of the formalism. The system enumerates all possible interpretations according to a set of constraints, meaning a less restricted rule set leads to a greater number of interpretations. For the example domain, sentence level discourse-structural annotations are then used to predict summary-worthy content. The results of this study are analysed in three parts. First, I examine the relative utility of individual discourse features and provide a qualitative discussion of these results. Second, the predictive abilities of these features are compared when they are manually annotated to when they are annotated with varying degrees of automation. Third, these results are compared to the predictive capabilities of classic extractive algorithms. I show that discourse features can be used to more accurately predict summary-worthy content than classic extractive algorithms. This holds true for automatically obtained annotations, but with a much clearer difference when using manual annotations. The classifiers learned in the prediction of summary-worthy sentences are subsequently used to inform the production of both extractive and abstractive summaries to a given length. A human-based evaluation is used to compare these summaries, as well as the outputs of a classic extractive summarizer. I analyse the impact of knowledge about discourse structure, obtained both manually and automatically, on summary production. This allows for some insight into the knock on effects on summary production that can occur from inaccurate discourse information (narrative structure and coreference information). My analyses show that even given inaccurate discourse information, the resulting abstractive summaries are considered more informative than their extractive counterparts. With human-level knowledge about discourse structure, these results are even clearer. In conclusion, this research provides a framework which can be used to detect the narrative structure of a text, and shows its potential to provide a more human-like approach to automatic summarization. I show the limit of what is achievable with this approach both when manual annotations are obtainable, and when only automatic annotations are feasible. Nevertheless, this thesis supports the suggestion that the future of summarization lies with abstractive and not extractive techniques