Proceedings of the conference. Association for Computational Linguistics. Meeting最新文献

The phenotypic complexity of Autism Spectrum Disorder motivates the application of modern computational methods to large collections of observational data, both for improved clinical diagnosis and for better scientific understanding. We have begun to create a corpus of annotated language samples relevant to this research, and we plan to join with other researchers in pooling and publishing such resources on a large scale. The goal of this paper is to present some initial explorations to illustrate the opportunities that such datasets will afford.

BLANC is a link-based coreference evaluation metric for measuring the quality of coreference systems on gold mentions. This paper extends the original BLANC ("BLANC-gold" henceforth) to system mentions, removing the gold mention assumption. The proposed BLANC falls back seamlessly to the original one if system mentions are identical to gold mentions, and it is shown to strongly correlate with existing metrics on the 2011 and 2012 CoNLL data.

The definitions of two coreference scoring metrics- B³ and CEAF-are underspecified with respect to predicted, as opposed to key (or gold) mentions. Several variations have been proposed that manipulate either, or both, the key and predicted mentions in order to get a one-to-one mapping. On the other hand, the metric BLANC was, until recently, limited to scoring partitions of key mentions. In this paper, we (i) argue that mention manipulation for scoring predicted mentions is unnecessary, and potentially harmful as it could produce unintuitive results; (ii) illustrate the application of all these measures to scoring predicted mentions; (iii) make available an open-source, thoroughly-tested reference implementation of the main coreference evaluation measures; and (iv) rescore the results of the CoNLL-2011/2012 shared task systems with this implementation. This will help the community accurately measure and compare new end-to-end coreference resolution algorithms.

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.

SPARQL queries have become the standard for querying linked open data knowledge bases, but SPARQL query construction can be challenging and time-consuming even for experts. SPARQL query generation from natural language questions is an attractive modality for interfacing with LOD. However, how to evaluate SPARQL query generation from natural language questions is a mostly open research question. This paper presents some issues that arise in SPARQL query generation from natural language, a test suite for evaluating performance with respect to these issues, and a case study in evaluating a system for SPARQL query generation from natural language questions.

Electronic health records (EHRs) contain important clinical information about patients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.