Animation Motion in NarrativeML

This paper describes qualitative spatial representations relevant to cartoon motion incorporated into NarrativeML, an annotation scheme intended to capture some of the core aspects of narrative. These representations are motivated by linguistic distinctions drawn from cross-linguistic studies. Motion is modeled in terms of transitions in spatial configurations, using an expressive dynamic logic with the manner and path of motion being derived from a few basic primitives. The manner is elaborated to represent properties of motion that bear on character affect. Such representations can potentially be used to support cartoon narrative summarization and question-answering. The paper discusses annotation challenges, and the use of computer vision to help in annotation. Work is underway on annotating a cartoon corpus in terms of this scheme

Content Selection for Timeline Generation from Single History Articles

This thesis investigates the problem of content selection for timeline generation from single history articles. While the task of timeline generation has been addressed before, most previous approaches assume the existence of a large corpus of history articles from the same era. They exploit the fact that salient information is likely to be mentioned multiple times in such corpora. However, large resources of this kind are only available for historical events that happened in the most recent decades. In this thesis, I present approaches which can be used to create history timelines for any historical period, even for eras such as the Middle Ages, for which no large corpora of supplementary text exist. The thesis first presents a system that selects relevant historical figures in a given article, a task which is substantially easier than full timeline generation. I show that a supervised approach which uses linguistic, structural and semantic features outperforms a competitive baseline on this task. Based on the observations made in this initial study, I then develop approaches for timeline generation. I find that an unsupervised approach that takes into account the article's subject area outperforms several supervised and unsupervised baselines. A main focus of this thesis is the development of evaluation methodologies and resources, as no suitable corpora existed when work began. For the initial experiment on important historical figures, I construct a corpus of existing timelines and textual articles, and devise a method for evaluating algorithms based on this resource. For timeline generation, I present a comprehensive evaluation methodology which is based on the interpretation of the task as a special form of single-document summarisation. This methodology scores algorithms based on meaning units rather than surface similarity. Unlike previous semantic-units-based evaluation methods for summarisation, my evaluation method does not require any manual annotation of system timelines. Once an evaluation resource has been created, which involves only annotation of the input texts, new timeline generation algorithms can be tested at no cost. This crucial advantage should make my new evaluation methodology attractive for the evaluation of general single-document summaries beyond timelines. I also present an evaluation resource which is based on this methodology. It was constructed using gold-standard timelines elicited from 30 human timeline writers, and has been made publicly available. This thesis concentrates on the content selection stage of timeline generation, and leaves the surface realisation step for future work. However, my evaluation methodology is designed in such a way that it can in principle also quantify the degree to which surface realisation is successful

Automatic recognition and normalization of temporal expressions in Serbian unstructured newspaper and medical texts

Ljudi u svakodnevnom životu koriste vreme kao univerzalni referentni sistem, u okviru koga se doga¯daji ili stanja nižu jedan za drugim, utvr¯duje dužina njihovog trajanja i navodi kada se neki doga¯daj desio. Znaˇcenje vremena i naˇcin na koji ˇcovek poima vreme ogledaju se i u komunikaciji, pre svega, u jeziˇckim izrazima koji se uˇcestalo koriste u svakodnevnom govoru. Vremenski izrazi, kao fraze prirodnog jezika koje na direktan naˇcin ukazuju na vreme, pružaju informaciju o tome kada se nešto dogodilo, koliko dugo je trajalo ili koliko ˇcesto se dešava. Uporedo s razvojem informatiˇckog društva, pove´cava se i koliˇcina slobodno dostupnih digitalnih informacija, što daje ve´ce mogu´cnosti pronalaženja potrebnih informacija, ali i utiˇce na složenost ovog procesa, iziskuju´ci koriš´cenje naprednih raˇcunarskih alata i mo´cnijih metoda automatske obrade tekstova prirodnih jezika. S obzirom na to da se znaˇcenje ve´cine elektronskih informacija menja u zavisnosti od vremena iskazanog u njima, radi uspešnog razumevanja tekstova pisanih prirodnim jezikom, neophodno je koriš´cenje alata koji su sposobni da automatski oznaˇce i informacije koje referišu na vreme i omogu´ce uspostavljanje hronološkog sleda opisanih doga¯daja. Stoga je potrebno razviti alate namenjene ekstrakciji vremenskih izraza, kod kojih su preciznost i odziv na visokom nivou i koji se brzo i jednostavno mogu prilagoditi novim zahtevima ili tekstovima drugog domena. Postojanje ovakvog sistema može u velikoj meri uticati na poboljšanje uˇcinka primene mnogih drugih aplikacija iz oblasti jeziˇckih tehnologija (ekstrakcija informacija, pronalaženje informacija, odgovaranje na pitanja, rezimiranje teksta itd.), ali i doprineti oˇcuvanju srpskog jezika u savremenom digitalnom okruženju...People in everyday life use time as a universal reference system, within which, events or states are sequenced one after the other, it is established how long they lasted and it is stated when an event occurred. The meaning of time and the way humans perceive time is reflected in communication, most of all, in linguistic expressions frequently used in everyday speech. Temporal expressions, as natural language phrases which directly refer to time, provide information on when something happened, how long it lasted and how often it occurs. Alongside with the information society development, the amount of freely available digital information has increased, which provides a greater possibility of finding the necessary information, but also affects the complexity of this process, by requiring the use of advanced computer tools and more powerful natural language text processing methods. Having in mind that the meaning of most electronic information can change depending on time expressed in them, it is essential to use tools which can both automatically mark the information related to time and enable the establishment of chronological order of described events. Therefore, it is necessary to develop tools for extraction of temporal expressions with high levels of precision and recall, which can be easily and quickly adapted to new demands and texts from different domains. The existence of such a system can, to a great extent, affect the effectiveness improvement in implementation of many other applications from the field of language technology (information extraction, information retrieval, question answering, text summarization, etc.), but also contribute to the preservation of the Serbian language in the contemporary digital environment..