We present a new method for the classification of “noisy” documents, based on filtering contents with bigrams and named entities. The method is applied to call for tender documents, but we claim it would be useful for many other Web collections, which also contain nontopical contents. Different variations of the method are discussed. We obtain the best results by filtering out a window around t...

Named Entity Recognisers (NERs) are typically used by question answering (QA) systems as means to preselect answer candidates. However, there has not been much work on the formal assessment of the use of NERs for QA nor on their optimal parameters. In this paper we investigate the main characteristics of a NER for QA. The results show that it is important to maintain high recall to retain all p...

This paper mainly describes a Chinese named entity recognition (NER) system NER@ISCAS, which integrates text, partof-speech and a small-vocabularycharacter-lists feature and heristic postprocess rules for MSRA NER open track under the framework of Conditional Random Fields (CRFs) model.

This paper investigates three multilingual named entity corpora, including named people, named locations and named organizations. Frequency-based approaches with and without dictionary are proposed to extract formulation rules of named entities for individual languages, and transformation rules for mapping among languages. We consider the issues of abbreviation and compound keyword at a distance.

Query classification is a subtask of 1CLICK task for selecting appropriate strategy to generate output text. In this paper, we propose to use named entity recognition tools and clue keywords (occupation name list and location type name list) to identify query types.

Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. Using wellunderstood techniques, we set out to improve Twitter NER performance when given a small set of annotated training tweets. To levera...

This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating. We outline the shared task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.

We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.

In this paper, we first create a Cyrillic Mongolian named entity manually annotated corpus. The annotation types contain person names, location names, organization names and other proper names. Then, we use Condition Random Field as classifier and design few categories features of Mongolian, including orthographic feature, morphological feature, gazetteer feature, syllable feature, word cluster...

In this paper we propose a method to automatically label multi-lingual data with named entity tags. We build on prior work utilizing Wikipedia metadata and show how to effectively combine the weak annotations stemming from Wikipedia metadata with information obtained through English-foreign language parallel Wikipedia sentences. The combination is achieved using a novel semi-CRF model for forei...