CLASSIFYING THE QUESTION BASED ON RULES THAT ASSOCIATE EXTRACTED WO...

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