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A document surrogate is usually represented in a list of words. Because not all words in a document reflect its content, it is necessary to select important words from the document that relate to its content. Such important words are called keywords and are selected with a particular equation based on Term Frequency
The tool for keyword extraction developed within the AXMEDIS project have been designed for working in a multilingual environment and new algorithms have been developed to generate keywords with higher representativeness for content search and identification. The paper specifies the linguistic criteria followed for
taken into account when indexing documents and when performing searching. Utilizing this approach, it is possible to use a natural language to express user queries. In many cases, this way is more usual for users to describe their information needs compared to the keyword style. The factoid question answering task is one
. Experiments carried out on conversational corpora for the keyword spotting task in the Chinese 2005 863 Evaluation show that this method can not only yield highly compact SCN lattices with syllable graph density (SGD) of 3.83, but also achieve an equal error rate (EER) of 32.45%, which is about 33% relatively reduction when
models such as a vector space model, a language model and two probabilistic models. We also proposed different measures to compute textual entailment between two terms allowing us to hopefully select appropriate keywords from thesauri to expand documents or queries automatically.
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