A new ontology-based question expansion （OBQE） method is proposed for question similarity calculation in a frequently asked question （FAQ） answering system. Traditional question similarity calculation methods use ...A new ontology-based question expansion （OBQE） method is proposed for question similarity calculation in a frequently asked question （FAQ） answering system. Traditional question similarity calculation methods use ＂word＂ to compose question vector, that the semantic relations between words are ignored. OBQE takes the relation as an important part. The process of the new system is：① to build two-layered domain ontology referring to WordNet and domain corpse;② to expand question trunks into domain cases;③ to use domain case composed vector to calculate question similarity. The experimental result shows that the performance of question similarity calculation with OBQE is being improved.展开更多
Community-based question answer（CQA） makes a figure network in development of social network. Similar question retrieval is one of the most important tasks in CQA. Most of the previous works on similar question retr...Community-based question answer（CQA） makes a figure network in development of social network. Similar question retrieval is one of the most important tasks in CQA. Most of the previous works on similar question retrieval were given with the underlying assumption that answers are similar if their questions are similar, but no work was done by modeling similarity measure with the constraint of the assumption. A new method of modeling similarity measure is proposed by constraining the measure with the assumption, and employing ensemble learning to get a comprehensive measure which integrates different context features for similarity measuring, including lexical, syntactic, semantic and latent semantic. Experiments indicate that the integrated model could get a relatively high performance consistence between question set and answer set. Models with better consistency tend to get a better precision according to answers.展开更多
文摘A new ontology-based question expansion （OBQE） method is proposed for question similarity calculation in a frequently asked question （FAQ） answering system. Traditional question similarity calculation methods use ＂word＂ to compose question vector, that the semantic relations between words are ignored. OBQE takes the relation as an important part. The process of the new system is：① to build two-layered domain ontology referring to WordNet and domain corpse;② to expand question trunks into domain cases;③ to use domain case composed vector to calculate question similarity. The experimental result shows that the performance of question similarity calculation with OBQE is being improved.
基金supported by the National Natural Science Foundation of China(61273365)the National High Technology Research and Development Program of China(2012AA011104)
文摘Community-based question answer（CQA） makes a figure network in development of social network. Similar question retrieval is one of the most important tasks in CQA. Most of the previous works on similar question retrieval were given with the underlying assumption that answers are similar if their questions are similar, but no work was done by modeling similarity measure with the constraint of the assumption. A new method of modeling similarity measure is proposed by constraining the measure with the assumption, and employing ensemble learning to get a comprehensive measure which integrates different context features for similarity measuring, including lexical, syntactic, semantic and latent semantic. Experiments indicate that the integrated model could get a relatively high performance consistence between question set and answer set. Models with better consistency tend to get a better precision according to answers.
