Two main challenges are the errors propagated from named entity recognition ( NER ) and the dearth of information in a single tweet.

We evaluate our method on a manually annotated data set, and show that our method outperforms the baseline that handles these two tasks separately, boosting the F1 from 80.2% to 83.6% for NER , and the Accuracy from 79.4% to 82.6% for NEN, respectively.

As a result, the task of named entity recognition ( NER ) for tweets, which aims to identify mentions of rigid designators from tweets belonging to named-entity types such as persons, organizations and locations (2007), has attracted increasing research interest.

Traditionally, NEN is regarded as a septated task, which takes the output of NER as its input (Li et al., 2002; Cohen, 2005; Jijkoun et al., 2008; Dai et al., 2011).

One limitation of this cascaded approach is that errors propagate from NER to NEN and there is no feedback from NEN to NER .

MaXEnt Time is the discriminative model with rich time features (but not NER) as described in Section 3.3.2 (Time+NER includes NER ).

7%, and adding NER by another 6%.

However, we instead propose using NER labels to extract what may have counted as collocations in their data.

We compare the NER features against the Unigram and Filtered NLLR models in our final experiments.

We use the freely available Stanford Parser and NER system1 to generate the syntactic interpretation for these features.

Table 5 further shows results on NER , the task of recovering the sets of named entity mentions pertaining to each target field.

Interestingly, several researchers have attempted to model label consistency and high-level relational constraints using state-of-the-art sequential models of named entity recognition ( NER ).

We will show that this approach yields better performance on the CMU seminar announcement dataset when evaluated in terms of NER .

Our approach is complimentary to NER methods, as it can consolidate noisy overlapping predictions from multiple systems into coherent sets.

We used a set of rules to extract candidate named entities per the types specified in Figure 2.4 The rules encode information typically used in NER , including content and contextual patterns, as well as lookups in available dictionaries (Finkel et al., 2005; Minkov et al., 2005).

Lexical features of this form are commonly used in NER (Finkel et al., 2005; Minkov et al., 2005).

(2005), applied sequential models to perform NER on this dataset, identifying named entities that pertain to the template slots.

The Figure also shows the results of the Stanford NER tagger for English (Finkel et al., 2005) (we used the MUC-7 classifier).

Named Entity Recognition ( NER ) is a frequently needed technology in NLP applications.

State-of-the-art statistical models for NER typically require a large amount of training data and linguistic expertise to be sufficiently accurate, which makes it nearly impossible to build high-accuracy models for a large number of languages.

Recently, there have been two lines of work which have offered hope for creating NER analyzers in many languages.

Table 3: Some of the entities identified using NER and NP Chunking in a discussion thread about the US 2012 elections

In addition to this shallow parsing method, we also use named entity recognition ( NER ) to identify more entities.

Now, both mentions of Obama will be recognized by the Stanford NER system and will be identified as one entity.

Although using both named entity recognition ( NER ) and noun phrase chunking achieves better results, it

can also be noted from the results that NER contributes more to the system performance.