We study the task of entity linking for tweets, which tries to associate each mention in a tweet with a knowledge base entry.

Following most existing studies, we choose Wikipedia as our knowledge base” .

In this work, we study the entity linking task for tweets, which maps each entity mention in a tweet to a unique entity, i.e., an entry ID of a knowledge base like Wikipedia.

linking task is generally considered as a bridge between unstructured text and structured machine-readable knowledge base , and represents a critical role in machine reading program (Singh et al., 2011).

Current entity linking methods are built on top of a large scale knowledge base such as Wikipedia.

0 Total is the total number of knowledge base entities;

5 TAB (http://www.w3.org/2002/05/tapl) is a shallow knowledge base that contains a broad range of lexical and taxonomic information about popular objects like music, movies, authors, sports, autos, health, etc.

(2012) propose LIEGE, a framework to link the entities in web lists with the knowledge base , with the assumption that entities mentioned in a Web list tend to be a collection of entities of the same conceptual type.

Here, an entity refers to an item of a knowledge base .

Following most existing work, we use Wikipedia as the knowledge base , and an entity is a definition page in Wikipedia; a mention denotes a sequence of tokens in a tweet that can be potentially linked to an entity.

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can generalize to previously unseen named entities.

One such language is Arabic, which: a) lacks a capitalization feature; and b) has relatively small knowledge bases , such as Wikipedia.

In this work we address both problems by incorporating cross-lingual features and knowledge bases from English using cross—lingual links.

In this paper, we presented different cross-lingual features that can make use of linguistic properties and knowledge bases of other languages for NER.

We used English as the “helper” language and we exploited the English capitalization feature and an English knowledge base , DBpedia.

DBpedia2 is a large collaboratively-built knowledge base in which structured information is extracted from Wikipedia (Bizer et al., 2009).

- Using cross-lingual links to exploit a large knowledge base , namely English DBpedia, to benefit NER.

Methods for information extraction (IE) and knowledge base (KB) construction have been intensively studied.

We infer type disjointness constraints from the YAG02 knowledge base using occurrence statistics.

This paper addressed the problem of detecting and semantically typing newly emerging entities, to support the life-cycle of large knowledge bases .

[9) The noun phrase is a known entity that can be directly mapped to the knowledge base .

d) The noun phrase is a new entity not known to the knowledge base at all.

To decide if a noun phrase is a true entity (i.e., an individual entity that is a member of one or more lexical classes) or a nonentity (i.e., a common noun phrase that denotes a class or a general concept), we base the decision on the following hypothesis (inspired by and generalizing (Bunescu 2006): A given noun phrase, not known to the knowledge base , is a true entity if its headword is singular and is consistently capitalized (i.e., always spelled with the first letter in upper case).

A large number of knowledge base (KB) construction projects have recently emerged.

In theory, WikiCiKE can be applied to any two wiki knowledge based of different languages.

2.1 Wiki Knowledge Base and Wiki Article

We consider each language version of Wikipedia as a wiki knowledge base , which can be represented as K = {ai H21, where at, is a disambiguated article in K and p is the size of K.

For example, K 3 indicates the source wiki knowledge base, and KT denotes the target wiki knowledge base .

The learned representation of entity is compact and can scale to very large knowledge base .

It is an essential first step for succeeding subtasks in knowledge base construction (Ji and Grishman, 2011) like populating attribute to entities.

However, the one-topic-per-entity assumption makes it impossible to scale to large knowledge base , as every entity has a separate word distribution P(w|e); besides, the training objective does not directly correspond with disambiguation performances.

While the first two can be improved by, say, using a better named entity tagger, incorporating other knowledge bases and building a question classifier, how to solve the third problem is tricky.

Probase is a knowledge base that establishes connections between 2.7 million concepts, discovered automatically by applying Hearst patterns (Hearst, 1992) to 1.68 billion Web pages.

Its abundant concept coverage distinguishes it from other knowledge bases , such as Freebase (Bollacker et al., 2008) and WikiTaxon-omy (Ponzetto and Strube, 2007).