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IOB1IOB2IOE1IOE2IOBESs1Commuting by car...OOOOOQuestionSegment 1s2To employees ...IBIESs3My company...OOOOOs4Managers...IBIIBQuestionSegment 2s5 ...IIIIIs6 Do you know ...IIEEEQuestionSegment 3s7How are a few...BBIESs8...OOOOOs9 For example,...IBIIBQuestionSegment 4s10 In this method...IIIEEs11 If you have any...OOOOOFigure 3.4. Example of Assignment of Chunk Labels.Actual tagging by IOB1, IOB2, IOE1, IOE2, and IOBES are shown in Figure3.4.In order to indicate the question type of chunk, a tag indicating the questiontype is linked to a tag indicating portion in the chunk such as B, E, I, O and Swith a hyphen “-”. For example, the B-W of IOB2 in Figure 3.4 is given at thestart sentence of question segment 4.3.4.2 Overview of the proposed techniqueThe processing flow in the proposed technique of chunking follows the steps inthe list below.32
Step 1 Segment a question article into sentences. Each segment is terminatedwith a period “.”.Step 2 Carry out chunking by article.Step 3 Extract question segments as chunks, identify the question types, andoutput them in pairs.Chunker divides a sequence of sentences into question segments and otherchunks. A chunk tag is given to each sentence. The chunk tags used are ofthe five types explained in Section 3.4.1, namely IOB1, IOB2, IOE1, IOE2, andIOBES, and the IO-tag that does not distinguish the B/E/S tags from the others.Sentences not involved in the identification of question types are given the O-tag.Those sentences that constitute a question segment are given a tag consisting ofthe combination of one of the letters I, B, E, and S and one of the letters Wand D, thus I-W and B-D for example, to represent the portion in the chunk andthe question type. Figure 3.5 shows an example of composition of chunks usingthe IOB-tags. A chunker learns a chunking model from the pairs of sentencesand their chunk tags in Figure 3.5. To extract question segments from a query,sentence labeling, that labels a chunk tag to a sentence, is firstly performed.Subsequently, sentences labeled same roles such as “-D” and “-W” are chunkedby post-processing. Consequently, a question segment is extracted as a chunk andthe question type is given to the question segment based on the label of chunk.3.4.3 Conditional random fieldThe CRF (Conditional Random Field) is a stochastic model for sets. Combinationsof two random variables to represent the properties of a set are associatedwith each other as a conditional probability [Lafferty 2001]. The CRF supposes arandom field that has the Markov property regarding the elements of a set to beobserved. The advantages of this are as follows: (1) There is no need to assumethe independency of random variables as with those in the Markov model; (2)Since a model is described with conditional random variables, the model parameterscan be estimated without calculating the distribution of random variablesin the condition.33
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[47] Chiori Hori, Takaaki Hori, Hid
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[84] Christopher D. Manning and Hin
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[122] Akihiro Shinmori, Manabu Okum
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