In Japanese scientific news articles, although the research results are described clearly, the article's sources tend to be uncited. This makes it difficult for readers to know the details of the research. In this paper, we address the task of extracting journal names from Japanese scientific news articles. We hypothesize that a journal name is likely to occur in a specific context. To support the hypothesis, we construct a character-based method and extract journal names using this method. This method only uses the left and right context features of journal names. The results of the journal name extractions suggest that the distribution hypothesis plays an important role in identifying the journal names.
Many bootstrap methods for extracting named entities have been proposed by various studies. In reference @cite_9 , a two-tiered bootstrapping process is proposed. The first level of this bootstrapping process extracts unique patterns and domain-specific terms simultaneously. The second level of this bootstrapping process deletes ambiguous terms from the dictionary. In reference @cite_6 , a method to classify the named entities using their spellings and the context in which they occur is proposed. In reference @cite_5 , using the fixed-length contexts around the named entities as contextual patterns is proposed. Note that the patterns used in this method are only on one side of the left or right contexts. In reference @cite_4 , a method that extracts the semantic relationships from the general contexts using large-scale resources, such as web resources, is proposed. This study also adopts a bootstrap method. In our method, contextual patterns are identified from not only one side, but both sides of the context.
@cite_5 @cite_9 @cite_4 @cite_6
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Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories. Bootstrapped pattern learning for entity extraction usually starts with seed entities and iteratively learns patterns and entities from unlabeled text. Patterns are scored by their ability to extract more positive entities and less negative entities. A problem is that due to the lack of labeled data, unlabeled entities are either assumed to be negative or are ignored by the existing pattern scoring measures. In this paper, we improve pattern scoring by predicting the labels of unlabeled entities. We use various unsupervised features based on contrasting domain-specific and general text, and exploiting distributional similarity and edit distances to learned entities. Our system outperforms existing pattern scoring algorithms for extracting drug-andtreatment entities from four medical forums. This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles. The usual approach to named-entity detection is to learn extraction rules that rely on linguistic, syntactic, or document format patterns that are consistent across a set of documents. However, when there is no consistency among documents, it may be more effective to learn document-specific extraction rules.This paper presents a knowledge-based approach to learning rules for named-entity extraction. Document-specific extraction rules are created using a generate-and-test paradigm and a database of known named-entities. Experimental results show that this approach is effective on Web documents that are difficult for the usual methods.