Name Entity Recognition and Binary Relation Detection for News Query

This paper proposes an innovative slant on name entity recognition (NER) and binary relation detection (BRD) for current news search and retrieval. The main contribution is to apply MITIE tools to find relevant news according to the given news stories. BRD can retrieve relevant news according to the relationship between given query term and news dataset. Another salient contribution is to accomplish news story agency classification with Stanford classifier and automatic document classification tool WEKA. Given training data, many news stories with agency class labels, WEKA can predict and identify the test news stories’ class labels. The evaluation of relevant news is based on MAP@100 in 20 queries given 160000 news story dataset. The retrieved results of all the queries can demonstrate up to 100 relevant stories in terms of theirs IDs. The evaluation of news agency classification is based on accuracy with correct classified news stories. Scoring with NER yields the better performance. Introduction This paper applies multiple kinds of advance information retrieval and extraction tool in the state of art information extraction tools. The MITIE [1] includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors. For query generation, it is common in the IR literature [2] the inverse document frequency idf of a term is a function of the frequency f of term in the collection and the number N of documents in the collection. They use the function log (N/(f+1)). Baseline algorithm A1-BASE [3] is a simple tf.idf based algorithm. It weights each term by tf.idf, where tf is the frequency of term in the text segment T. This result in larger weights for terms that appear more frequently in T, and larger weights for more distinctive terms of the news story is more likely to find articles related to the story. The baseline algorithm returns two terms with largest weight as the query. Another baseline algorithm is that Term is weighted by tf.idf 2 . Named entities are important for issuing focused queries. Idf component is more important than tf. The stem of a word is approximated by taking the first 5 letters of the word. Congress and congressional would share the same stem, congr. The intention is to aggregate the weight of terms that describe the same entity. The weight of a term is c.tf.idf 2 , where c=1 if term was a noun and c=0.5 otherwise. We use this weighting scheme since nouns are often more useful in queries than others. The weight of a stem is the sum of the weights of its terms. To issue a query, algorithm determines the two top-weighted stems and top-weighted term for each of these stems. These two terms form the query. Stems are computed by stemming both words. It finds the top-weighted term for two top-weighted stems. The main contributions can be summarized as below. 1). The innovative application of NER and BRD into news retrieval is proposed. 2). MITIE tools have been applied to find relevant news. 3). The proposed method conquers language barrier to support search in multiple version language. MITIE tools are originally support English character only. Nevertheless, it is difficult for