709 research outputs found
Learning Character-level Compositionality with Visual Features
Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.Comment: Accepted to ACL 201
Extracting fine-grained economic events from business news
Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also
disinformation. One prominent case of an event leading to circulation of
disinformation on social media is the MH17 plane crash. Studies analysing the
spread of information about this event on Twitter have focused on small,
manually annotated datasets, or used proxys for data annotation. In this work,
we examine to what extent text classifiers can be used to label data for
subsequent content analysis, in particular we focus on predicting pro-Russian
and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though
we find that a neural classifier improves over a hashtag based baseline,
labeling pro-Russian and pro-Ukrainian content with high precision remains a
challenging problem. We provide an error analysis underlining the difficulty of
the task and identify factors that might help improve classification in future
work. Finally, we show how the classifier can facilitate the annotation task
for human annotators
A pragmatic guide to geoparsing evaluation
Abstract: Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems. Evaluation is further made inconsistent, even unrepresentative of real world usage by the lack of distinction between the different types of toponyms, which necessitates new guidelines, a consolidation of metrics and a detailed toponym taxonomy with implications for Named Entity Recognition (NER) and beyond. To address these deficiencies, our manuscript introduces a new framework in three parts. (Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms. (Part 2) Metrics: discussed and reviewed for a rigorous evaluation including recommendations for NER/Geoparsing practitioners. (Part 3) Evaluation data: shared via a new dataset called GeoWebNews to provide test/train examples and enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping and evaluating machine learning NLP models
Indonesian Named-entity Recognition for 15 Classes Using Ensemble Supervised Learning
AbstractHere, we describe our effort in building Indonesian Named Entity Recognition (NER) for newspaper article with 15 classes which is larger number of class type compared to existing Indonesian NER. We employed supervised machine learning in the NER and conducted experiments to find the best attribute combination and the best algorithm with highest accuracy. We compared the attribute of word level, sentence level and document level. In the algorithm, we compared several single machine learning algorithms and also an ensembled one. Using 457 news articles, the best accuracy was achieved by using ensemble technique where the result of several machine learning algorithms were used as the feature for one machine learning algorithm
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