1,415 research outputs found
Learning Dynamic Feature Selection for Fast Sequential Prediction
We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.Comment: Appears in The 53rd Annual Meeting of the Association for
Computational Linguistics, Beijing, China, July 201
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
Visual Interactive Comparison of Part-of-Speech Models for Domain Adaptation
Interactive visual analysis of documents relies critically on the ability of machines to process and analyze texts. Important techniques for text processing include text summarization, classification, or translation. Many of these approaches are based on part-of-speech tagging, a core natural language processing technique. Part-of-speech taggers are typically trained on collections of modern newspaper, magazine, or journal articles. They are known to have high accuracy and robustness when applied to contemporary newspaper style texts. However, the performance of these taggers deteriorates quickly when applying them to more domain specific writings, such as older or even historical documents. Large training sets tend to be scarce for these types of texts due to the limited availability of source material and costly digitization and annotation procedures. In this paper, we present an interactive visualization approach that facilitates analysts in determining part-of-speech tagging errors by comparing several standard part-of-speech tagger results graphically. It allows users to explore, compare, evaluate, and adapt the results through interactive feedback in order to obtain a new model, which can then be applied to similar types of texts. A use case shows successful applications of the approach and demonstrates its benefits and limitations. In addition, we provide insights generated through expert feedback and discuss the effectiveness of our approach
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