Question: What is used for named entity recognition?
Evidence: ClaimBuster-based (1,045 S features; core): First, in order to be able to compare our model and features directly to the previous state of the art, we re-implemented, to the best of our ability, the sentence-level features of ClaimBuster as described in BIBREF9, namely TF.IDF-weighted bag of words (998 features), part-of-speech tags (25 features), named entities as recognized by Alchemy API (20 features), sentiment score from Alchemy API (1 feature), and number of tokens in the target sentence (1 feature).
Apart from providing means of comparison to the state of the art, these features also make a solid contribution to the final system we build for check-worthiness estimation. However, note that we did not have access to the training data of ClaimBuster, which is not publicly available, and we thus train on our dataset (described above).
Sentiment (2 S features): Some sentences are highly negative, which can signal the presence of an interesting claim to check, as the two example sentences below show (from the 1st and the 2nd presidential debates):

We used the NRC sentiment lexicon BIBREF15 as a source of words and $n$-grams with positive/negative sentiment, and we counted the number of positive and of negative words in the target sentence. These features are different from those in the CB features above, where these lexicons were not used.
Named entities (NE) (1 S feature): Sentences that contain named entity mentions are more likely to contain a claim that is worth fact-checking as they discuss particular people, organizations, and locations. Thus, we have a feature that counts the number of named entity mentions in the target sentence; we use the NLTK toolkit for named entity recognition BIBREF16. Unlike the CB features above, here we only have one feature; we also use a different toolkit for named entity recognition.
Linguistic features (9 S features): We count the number of words in each sentence that belong to each of the following lexicons: Language Bias lexicon BIBREF17, Opinion Negative and Positive Words BIBREF18, Factives and Assertive Predicates BIBREF19, Hedges BIBREF20, Implicatives BIBREF21, and Strong and Weak subjective words. Some examples are shown in Table TABREF12.
Tense (1 S feature): Most of the check-worthy claims mention past events. In order to detect when the speaker is making a reference to the past or s/he is talking about his/her future vision and plans, we include a feature with three values—indicating whether the text is in past, present, or future tense. The feature is extracted from the verbal expressions, using POS tags and a list of auxiliary verbs and phrases such as will, have to, etc.
Length (1 S feature): Shorter sentences are generally less likely to contain a check-worthy claim. Thus, we have a feature for the length of the sentence in terms of characters. Note that this feature was not part of the CB features, as there length was modeled in terms of tokens, but here we do so using characters.
Thus, we have a feature that counts the number of named entity mentions in the target sentence; we use the NLTK toolkit for named entity recognition (Loper and Bird, 2002).
Answer: The NLTK toolkit.