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Financial news articles exhibit sufficient information to disclose commercial entities and their behavior. In this paper, a method for commercial network construction is presented, in which natural language processing techniques are applied in commercial entity tagging and commercial relation mining to handle abbreviation recognition, co-reference resolution and contextual commercial relation mining...
Since whether or not a character sequence refers to an object in real word is determined mostly by its context, the context pattern induction plays an important role in entity recognition, which is an important task in the field of natural language processing (NLP). We present a nominal entity recognition method based on the context pattern induction. It induces high-precision context patterns in...
In this paper, we report our participation to the ESTER 2 (Evaluation des Systemes de Transcription Enrichie d'Emissions Radiophoniques) evaluation campaign, on the Named Entity Recognition for French track. After describing the ESTER 2 goals and guidelines, we present our deep robust parser. Then we show how we adapt the existing French NER module of this parser to the ESTER 2 task. The results we...
There are many connotative semantic features in Chinese which can help Chinese named entity recognition. Moreover, one of the important strongpoint of maximum entropy model is that it can syncretize features in different granularity and level. With that in mind, many Chinese named entity semantic knowledge bases were established by extracting information from corpus in this paper. However, because...
This paper presents a work achieved within a decision support system. The aim is to support an operator in his/her task of analyzing soft data to monitor and anticipate a geopolitical crisis. The incoming data is filtered and analyzed so as to retain only the relevant events. Therefore, the system extracts the relevant events from the incoming soft data and produces synthetic descriptions of these...
Most existing corpus based relation extraction techniques focus on predefined relations. In this paper, a clustering based method is presented for domain relevant relation extraction including both relation type discovery and relation instance extraction. Given two raw corpora, one in the general domain, one in an application domain, domain specific verbs connecting different instances are extracted...
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