71,481 research outputs found
A Bootstrapping architecture for time expression recognition in unlabelled corpora via syntactic-semantic patterns
In this paper we describe a semi-supervised approach to the extraction of time expression mentions in large unlabelled corpora based on bootstrapping.
Bootstrapping techniques rely on a relatively small amount of initial human-supplied examples (termed “seeds”) of the type of entity or concept to be learned, in order to capture an initial set of patterns or rules from the unlabelled text that extract the supplied data. In turn, the learned patterns are employed to find new potential examples, and the process is repeated to grow the set of patterns and (optionally) the set of examples. In order to prevent the learned pattern set from producing spurious results, it becomes essential
to implement a ranking and selection procedure to filter out “bad” patterns and, depending on the case, new candidate examples. Therefore, the type of patterns employed (knowledge representation) as well as the ranking and selection procedure are paramount to the quality of the results. We present a complete bootstrapping algorithm for recognition of time expressions, with a special emphasis on the type of patterns used (a combination of semantic and morpho- syntantic elements) and the ranking and selection criteria. Bootstrap-
ping techniques have been previously employed with limited success for several NLP problems, both of recognition and classification, but their application to time expression recognition is, to the best of our knowledge, novel. As of this writing, the described architecture is in the final stages of implementation, with experimention and evalution being already underway.Postprint (published version
USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding
This paper describes the University of Sheffield's entry in the 2011 TAC KBP
entity linking and slot filling tasks. We chose to participate in the
monolingual entity linking task, the monolingual slot filling task and the
temporal slot filling tasks. We set out to build a framework for
experimentation with knowledge base population. This framework was created, and
applied to multiple KBP tasks. We demonstrated that our proposed framework is
effective and suitable for collaborative development efforts, as well as useful
in a teaching environment. Finally we present results that, while very modest,
provide improvements an order of magnitude greater than our 2010 attempt.Comment: Proc. Text Analysis Conference (2011
Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations
This short paper investigates how locations in or close to
water masses in topics and documents (e.g. rivers, seas,
oceans) are referred to. For this study, 13 topics from the
GeoCLEF topics 2005-2008 aiming at documents on rivers,
oceans, or sea names were selected and the corresponding
relevant documents retrieved and manually annotated. Results of the geographic annotation indicate that i) topics aiming at locations close to water contain a wide variety of spatial relations (indicated by dierent prepositions), ii)
unnamed locations can be generated on-the-fly by referring
to movable objects (e.g. ships, planes) travelling along a
path, iii) underspecied regions are referenced by proximity
or distance or directional relations. In addition, several
generic expressions (e.g. "in international waters") are frequently used, but refer to different underspecified regions
Document Filtering for Long-tail Entities
Filtering relevant documents with respect to entities is an essential task in
the context of knowledge base construction and maintenance. It entails
processing a time-ordered stream of documents that might be relevant to an
entity in order to select only those that contain vital information.
State-of-the-art approaches to document filtering for popular entities are
entity-dependent: they rely on and are also trained on the specifics of
differentiating features for each specific entity. Moreover, these approaches
tend to use so-called extrinsic information such as Wikipedia page views and
related entities which is typically only available only for popular head
entities. Entity-dependent approaches based on such signals are therefore
ill-suited as filtering methods for long-tail entities. In this paper we
propose a document filtering method for long-tail entities that is
entity-independent and thus also generalizes to unseen or rarely seen entities.
It is based on intrinsic features, i.e., features that are derived from the
documents in which the entities are mentioned. We propose a set of features
that capture informativeness, entity-saliency, and timeliness. In particular,
we introduce features based on entity aspect similarities, relation patterns,
and temporal expressions and combine these with standard features for document
filtering. Experiments following the TREC KBA 2014 setup on a publicly
available dataset show that our model is able to improve the filtering
performance for long-tail entities over several baselines. Results of applying
the model to unseen entities are promising, indicating that the model is able
to learn the general characteristics of a vital document. The overall
performance across all entities---i.e., not just long-tail entities---improves
upon the state-of-the-art without depending on any entity-specific training
data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on
Information and Knowledge Management. 201
The Development of a Temporal Information Dictionary for Social Media Analytics
Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist
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