Text2KB Model Sample Clauses

Text2KB Model. ‌ Text2KB improves the baseline knowledge base question answering model by utilizing textual resources on various stages of the pipeline. Our model uses web search results to improve question analysis, i.e., identify question topic, and extract additional features to support generated answer xxxxx- dates. Question-answer pairs from CQA archives are used to learn asso- ciations between question words and KB predicates, and score candidate SPARQL queries. Finally, a text collection annotated with KB entity men- tions is used to build a language model for entity pairs, and generate answer ranking features. Web search results for question understanding and answer rank- ing. Traditional TextQA systems rely on search results to retrieve relevant documents, which are then used to extract answers to users’ questions. Rel- evant search results mention question entities multiple times and in various forms, which can be helpful for entity linking [57]. Furthermore, retrieved document set often contains multiple occurrences of the answer, which can be a strong signal for candidate ranking [126]. To obtain related web search results, Text2KB issues the question as a query to a search engine9, extracts top 10 result snippets and the corresponding documents. Next, Text2KB uses Aqqu entity linking module to detect KB entity mentions in both snippets and documents. Question text provides only a limited context for entity disambiguation and linking; additionally, the entity name can be misspelled or an uncommon variation used. This complicates the task of entity identification, which is the foundation of KB question answering process. Fortunately, web search results help with these problems, as they usually contain multiple mentions 9In our experiments, we use the Bing Web Search API xxxxx://xxx.xxxxxxxxx.xxx/cognitive-services/en-us/bing-web-search-api and local Wikipedia search using Lucene of the same entities and provide more context for disambiguation. Text2KB uses the search result snippets to expand the set of detected question entities. More specifically, we count the frequencies of each entity mentioned in search snippets, and most popular ones with names similar to some of the question terms are added to the list of topical entities. The goal of this similarity condition is to keep only entities that are likely mentioned in the question text and filter out related, but different entities. To estimate the similarity between a name and question tokens, we use Xxxx-X...
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