Entity extraction is fundamental to many text mining tasks such as organisation name recognition. A popular approach to entity extraction is based on matching sub-string candidates in a document against a dictionary of entities. To handle spelling errors and name variations of entities, usually the matching is approximate and edit or Jaccard distance is used to measure dissimilarity between sub-string candidates and the entities. For approximate entity extraction from free text, existing work considers solely character-based or solely token-based similarity and hence cannot simultaneously deal with minor variations at token level and typos. In this paper, we address this problem by considering both character-based similarity and token-based similarity (i.e. two-level similarity). Measuring one-level (e.g. character-based) similarity is computationally expensive, and measuring two-level similarity is dramatically more expensive. By exploiting the properties of the two-level similarity and the weights of tokens, we develop novel techniques to significantly reduce the number of sub-string candidates that require computation of two-level similarity against the dictionary of entities. A comprehensive experimental study on real world datasets show that our algorithm can efficiently extract entities from documents and produce a high F1 score in the range of [0.91, 0.97].
: The approximate entity extraction problem can be viewed as the approximate string matching problem which is a well-studied problem. Navarro gives a nice survey for the approximate string matching problem @cite_9 . Here, we focus on some recent work in entity extraction.
@cite_9
2010392031 2055261195 2099370490 1973702599
Approximate matching of strings is reviewed with the aim of surveying techniques suitable for finding an item in a database when there may be a spelling mistake or other error in the keyword. The methods found are classified as either equivalence or similarity problems. Equivalence problems are seen to be readily solved using canonical forms. For sinuiarity problems difference measures are surveyed, with a full description of the wellestablmhed dynamic programming method relating this to the approach using probabilities and likelihoods. Searches for approximate matches in large sets using a difference function are seen to be an open problem still, though several promising ideas have been suggested. Approximate matching (error correction) during parsing is briefly reviewed. Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies. Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance. In this paper, we study the problem of approximate dictionary matching with edit distance constraints. Compared to existing studies using token-based similarity constraints, our problem definition enables us to capture typographical or orthographical errors, both of which are common in entity extraction tasks yet may be missed by token-based similarity constraints. Our problem is technically challenging as existing approaches based on q-gram filtering have poor performance due to the existence of many short entities in the dictionary. Our proposed solution is based on an improved neighborhood generation method employing novel partitioning and prefix pruning techniques. We also propose an efficient document processing algorithm that minimizes unnecessary comparisons and enumerations and hence achieves good scalability. We have conducted extensive experiments on several publicly available named entity recognition datasets. The proposed algorithm outperforms alternative approaches by up to an order of magnitude. This paper presents a Named Entity Extraction (NEE) system for the CoNLL 2002 competition. The two main sub-tasks of the problem, recognition (NER) and classification (NEC), are performed sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary AdaBoost classifiers.