However, when we create feature vectors for the classifier, the seeds themselves are hidden and only contextual features are used to represent each training instance.

We use an in-house sentence segmenter and NP chunker to identify the base NPs in each sentence and create feature vectors that represent each constituent in the sentence as either an NP or an individual word.

Two training instances would be created, with feature vectors that look like this, where M represents a modifier inside the target NP:

To formalize this task, we use a feature vector w to represent a word 21) in question, and a binary variable 0 to indicate whether this word is correct or not.

In the feature vector , we look at 2 words before and 2 words after the current word position (w_2, w_1, 212,201,202).

We collect features {wd, p03, link, dwpp} for each word among these words and combine them into the feature vector w for 212.

Quite a few methods have been suggested (Lin and Pantel, 2001; Bhagat et al., 2007; Yates and Etzioni, 2009), which differ in terms of the specifics of the ways in which predicates are represented, the features that are extracted, and the function used to compute feature vector similarity.

When computing distributional similarity scores, a template is represented as a feature vector of the CUIs that instantiate its arguments.

Next, we represent each pair of propositional templates with a feature vector of various distributional similarity scores.

A template pair is represented by a feature vector where each coordinate is a different distributional similarity score.

Another variant occurs when using binary templates: a template may be represented by a pair of feature vectors , one for each variable (Lin and Pantel, 2001), or by a single vector, where features represent pairs of instantiations (Szpektor et al., 2004; Yates and Etzioni, 2009).

High-dimensional feature vectors with only several nonzero dimensions bring large time consumption to our model.

Thus it is necessary to reduce the dimension of the feature vectors .

In the bottom layer, the binary feature vectors based on the statistics of the word occurrence in the answers are used to compute the “hidden features” in the

j where 0'(x) = 1/ (l + e"‘), 3 denotes the visible feature vector of the answer, qi is the ith element of the question vector, and h stands for the hidden feature vector for reconstructing the questions.

To detect the best answer to a given question, we just have to send the vectors of the question and its candidate answers into the input units of the network and perform a level-by-level calculation to obtain the corresponding feature vectors .

In order to compute the dot product of the feature vectors in the exponentially high dimensional feature space, we introduce the tree kernel functions as follows:

It is infeasible to explicitly compute the kernel function by expressing the sub-trees as feature vectors .

In addition, explicitly utilizing syntactic tree fragments results in exponentially high dimensional feature vectors , which is hard to compute.

The feature vector of the classifier is computed using a composite kernel:

Furthermore, as the size of the feature vector K increases, the space becomes even more difficult to search.

Thus, we represent state/action pairs with a feature vector gb(s, a) E RK.

Learning exactly which words influence decision making is difficult; reinforcement learning algorithms have problems with the large, sparse feature vectors common in natural language processing.

For a given state 3 = (u, l, c) and action a = (l’, 0’), our feature vector gb(s, a) is composed of the following:

We tabulate the transitions of entities between different syntactic positions (or their nonoccurrence) in sentences, and convert the frequencies of transitions into a feature vector representation of transition probabilities in the document.

We solve this problem in a supervised machine learning setting, where the input is the feature vector representations of the two versions of the document, and the output is a binary value indicating the document with the original sentence ordering.

Transition length — the maximum length of the transitions used in the feature vector representation of a document.

We used CRFs-based Japanese dependency parser (Imamura et al., 2007) and named entity recognizer (Suzuki et al., 2006) for sentiment extraction and constructing feature vectors for readability score, respectively.

where, given two adjacent sentences 3,- and 3,41, ngb(sz-, 3H1), which measures the connectivity of the two sentences, is the inner product of w and gb(si, 3H1), w is a parameter vector and gb(si, 3141) is a feature vector of the two sentences.

We also define feature vector (13(8) of the entire sequence 8 = (so, 31, .

In standard text classification, a document d is represented under the bag-of-words model as |V|-dimensional feature vector x E X, where V, the vocabulary, denotes an ordered set of words, :ci 6 x denotes the normalized frequency of word 2' in d, and X is an inner product space.

D3 denotes the training set and comprises tuples of the form (X, 3/), which associate a feature vector x E X with a class label 3/ E Y.

A document d is described as normalized feature vector x under a unigram bag-of-words document representation.

Thus, it is computational infeasible to directly use the feature vector (MT).

Suppose the training set S consists of labeled vectors {(xi, 311)}, where xi is the feature vector

where a,- is the learned parameter for a feature vector xi, and b is another parameter which can be derived from a,- .