05151 Summary -- Annotating, Extracting and Reasoning about Time and Events

The main focus of the seminar was on TimeML-based temporal annotation and reasoning. We were concerned with three main points: determining how effectively one can use the TimeML language for consistent annotation, determining how useful such annotation is for further processing, and determining what modifications should be applied to the standard to improve its usefulness in applications such as question-answering and information retrieval

05151 Abstracts Collection -- Annotating, Extracting and Reasoning about Time and Events

From 10.04.05 to 15.04.05, the Dagstuhl Seminar 05151 ``Annotating, Extracting and Reasoning about Time and Events\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Exploring Different Dimensions of Attention for Uncertainty Detection

Neural networks with attention have proven effective for many natural language processing tasks. In this paper, we develop attention mechanisms for uncertainty detection. In particular, we generalize standardly used attention mechanisms by introducing external attention and sequence-preserving attention. These novel architectures differ from standard approaches in that they use external resources to compute attention weights and preserve sequence information. We compare them to other configurations along different dimensions of attention. Our novel architectures set the new state of the art on a Wikipedia benchmark dataset and perform similar to the state-of-the-art model on a biomedical benchmark which uses a large set of linguistic features.Comment: accepted at EACL 201

Dagstuhl News January - December 2005

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Biased modality and epistemic weakness with the future and MUST: non- veridicality, partial knowledge

We defend the view of epistemic `must' as weak and claim that `must p' is used when the speaker does not know p. Novel arguments for this well-known account are provided. The theory is extended to epistemic future

Temporal processing of news : annotation of temporal expressions, verbal events and temporal relations

The ability to capture the temporal dimension of a natural language text is essential to many natural language processing applications, such as Question Answering, Automatic Summarisation, and Information Retrieval. Temporal processing is a ¯eld of Computational Linguistics which aims to access this dimension and derive a precise temporal representation of a natural language text by extracting time expressions, events and temporal relations, and then representing them according to a chosen knowledge framework. This thesis focuses on the investigation and understanding of the di®erent ways time is expressed in natural language, on the implementation of a temporal processing system in accordance with the results of this investigation, on the evaluation of the system, and on the extensive analysis of the errors and challenges that appear during system development. The ultimate goal of this research is to develop the ability to automatically annotate temporal expressions, verbal events and temporal relations in a natural language text. Temporal expression annotation involves two stages: temporal expression identi¯cation concerned with determining the textual extent of a temporal expression, and temporal expression normalisation which ¯nds the value that the temporal expression designates and represents it using an annotation standard. The research presented in this thesis approaches these tasks with a knowledge-based methodology that tackles temporal expressions according to their semantic classi¯cation. Several knowledge sources and normalisation models are experimented with to allow an analysis of their impact on system performance. The annotation of events expressed using either ¯nite or non-¯nite verbs is addressed with a method that overcomes the drawback of existing methods v which associate an event with the class that is most frequently assigned to it in a corpus and are limited in coverage by the small number of events present in the corpus. This limitation is overcome in this research by annotating each WordNet verb with an event class that best characterises that verb. This thesis also describes an original methodology for the identi¯cation of temporal relations that hold among events and temporal expressions. The method relies on sentence-level syntactic trees and a propagation of temporal relations between syntactic constituents, by analysing syntactic and lexical properties of the constituents and of the relations between them. The detailed evaluation and error analysis of the methods proposed for solving di®erent temporal processing tasks form an important part of this research. Various corpora widely used by researchers studying di®erent temporal phenomena are employed in the evaluation, thus enabling comparison with state of the art in the ¯eld. The detailed error analysis targeting each temporal processing task helps identify not only problems of the implemented methods, but also reliability problems of the annotated resources, and encourages potential reexaminations of some temporal processing tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Domain-sensitive Temporal Tagging for Event-centric Information Retrieval

Temporal and geographic information is of major importance in virtually all contexts. Thus, it also occurs frequently in many types of text documents in the form of temporal and geographic expressions. Often, those are used to refer to something that was, is, or will be happening at some specific time and some specific place – in other words, temporal and geographic expressions are often used to refer to events. However, so far, event-related information needs are not well served by standard information retrieval approaches, which motivates the topic of this thesis: event-centric information retrieval. An important characteristic of temporal and geographic expressions – and thus of two components of events – is that they can be normalized so that their meaning is unambiguous and can be placed on a timeline or pinpointed on a map. In many research areas in which natural language processing is involved, e.g., in information retrieval, document summarization, and question answering, applications can highly benefit from having access to normalized information instead of only the words as they occur in documents. In this thesis, we present several frameworks for searching and exploring document collections with respect to occurring temporal, geographic, and event information. While we rely on an existing tool for extracting and normalizing geographic expressions, we study the task of temporal tagging, i.e., the extraction and normalization of temporal expressions. A crucial issue is that so far most research on temporal tagging dealt with English news-style documents. However, temporal expressions have to be handled in different ways depending on the domain of the documents from which they are extracted. Since we do not want to limit our research to one domain and one language, we develop the multilingual, cross-domain temporal tagger HeidelTime. It is the only publicly available temporal tagger for several languages and easy to extend to further languages. In addition, it achieves state-of-the-art evaluation results for all addressed domains and languages, and lays the foundations for all further contributions developed in this thesis. To achieve our goal of exploiting temporal and geographic expressions for event-centric information retrieval from a variety of text documents, we introduce the concept of spatio-temporal events and several concepts to "compute" with temporal, geographic, and event information. These concepts are used to develop a spatio-temporal ranking approach, which does not only consider textual, temporal, and geographic query parts but also two different types of proximity information. Furthermore, we adapt the spatio-temporal search idea by presenting a framework to directly search for events. Additionally, several map-based exploration frameworks are introduced that allow a new way of exploring event information latently contained in huge document collections. Finally, an event-centric document similarity model is developed that calculates document similarity on multilingual corpora solely based on extracted and normalized event information