2) classifying the question based on rules that associate extracted words to concepts.
H u a n g, T h i n t and Q i n [9] describe several statistical models for question
classification. Their models employ support vector machines and maximum entropy
models as the learning methods, and utilize a rich linguistic feature set including both
syntactic and semantic information. As a pioneer work, K i m [12] introduces a
general framework for sentence classification using CNNs. By stacking several
convolutional, max-over-time pooling, and fully connected layers, the proposed
model achieves impressive results on different sentence classification tasks.
Following the work of K i m [12], M a et al. [17] propose a novel model with group
sparse CNNs. L i g o z a t [16] presents a transfer learning model for question
classification. By automatically translating questions and labels from a source
language into a target language, the proposed method can build a question
classification in the target language without any annotated data.
NER in Questions. NER is a crucial component in most QA systems. M o l l a,
Z a a n e n and S m i t h [20] present an NER model for question answering that aims
at higher recall. Their model consists of two phases, which uses hand-written regular
expressions and gazetteers in the first phase and machine learning techniques in the
second phase. B a c h et al. [2] describe an empirical study on extracting important
information in transportation law questions. Using conditional random fields [13] as
the learning method, their model can extract 16 types of information with high
precision and recall. A b u j a b a l et al. [1], C o s t a [4], S h a r m a et al. [22],
S r i h a r i and L i [23] are some examples, among a lot of QA systems that we cannot
list, that exploit an NER component. In addition to studies on building QA systems,
several works have been conducted to provide benchmark datasets for the NER task
in the context of QA [11, 19]. M e n d e s, C o h e u r and L o b o [19] introduce nearly
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