Intelligent Disaster Response via Social Media Analysis A Survey <s> Sentence-Level Language Change <s> Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Sentence-Level Language Change <s> is one of the most recognizablecharacters in 20th century cinema. HAL is an artiﬁcial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment inthe plot, even reading lips. It is now clear that HAL’s creator, Arthur C. Clarke, wasa little optimistic in predicting when an artiﬁcial agent such as HAL would be avail-able. But just how far off was he? What would it take to create at least the language-relatedpartsofHAL?WecallprogramslikeHALthatconversewithhumansinnatural <s> BIB002
Sentence-Level language change measures the novelty of a sentence in comparison to a presumed set of sentences. For event detection in a region using Twitter, language model of a tweet is compared to the language model of the tweets that have been posted in normal situation from the same region. Kullback-Leibler (KL) divergence  is a measure that calculates divergence between two sentence-level language models, Θ1 and Θ2 as defined in Equation 1 . A sentence-level language model is a statistical model of sequences of words (i.e. sentences). As shown in Equation 2, the probability of observing a sentence, w1w2...wn, is calculated based on the assumption that probability of observing each of its words, wi, is dependent on the previous words.  A common method for calculating the probabilities in Equation 2 is Maximum Likelihood estimation. In this method, the probability of observing word wn after observing the sequence of w1...wn−1, p(wn|w1...wn−1), is calculated using the conditional probability in Equation  . Where C(wi...wj) is the frequency of observing the sequence of words wi to wj and W is any possible sequence of n − 1 words in the corpus. One challenge of using Equation 3 is calculating the denominator. Computing the denominator is computationally expensive in large corpora due to a large number of possible sentences. To overcome this challenge, n is usually set to 1, 2, or 3 which yields to unigram, bigram, or trigram language models respectively BIB002 . Another challenge is calculating the probability for the sequences (sentences) that have not been observed in the corpus. To overcome this problem, interpolation techniques can be used in which the probability of observing a sequence is calculated by using the probabilities of shorter sequences. For example, in a trigram language model, probability of observing a trigram can be calculated by mixing probabili-ties of bigrams and unigrams as shown in Equation BIB001 . p(wn|wn−2wn−1) = λ1p(wn|wn−2wn−1)+ λ2p(wn|wn−1) + λ3p(wn) (4)