A dependency relation defines a binary relation that describes whether a pairwise syntactic relation among two words holds in a sentence.

In ReNew, we exploit the Stanford typed dependency representations (de Marneffe et al., 2006) that use triples to formalize dependency relations .

tains three lists of dependency relations , associated respectvely with positive, neutral, or negative sentiment.

To do this, we first divide all features into four basic feature sets: T (transition cues), P (punctuations, special name-entities, and segment positions), G (grammar), and 0D (opinion words and dependency relations ).

Third, the lexicon generator determines which newly learned dependency relation triples to promote to the lexicon.

For each sentiment, the Triple Extractor (TE) extracts candidate dependency relation triples using a novel rule-based approach.

Table l: Dependency relation types used in extracting (E) and domain-specific lexicon (L).

(3) To capture the contextual sentiment of words, ReNew uses dependency relation pairs as the basic elements in the generated sentiment lexicon.

After classifying the sentiment of Segment 5 as NEG, we associate the dependency relation pairs {“sign”, “wear”} and {“sign”, “tear”} with that sentiment.

For example, The sixth feature in Table 5 represents that the dependency relation is preferred to be labeled Explanation with the fact that “because” is the first word of the dependent EDU.

Similar to the syntactic dependency structure defined by McDonald (2005a, 2005b), we insert an artificial EDU e0 in the beginning for each document and label the dependency relation linking from 60 as ROOT.

A labeled directed arc is used to represent the dependency relation from one head to its dependent.

Then, discourse dependency structure can be formalized as the labeled directed graph, Where nodes correspond to EDUs and labeled arcs correspond to labeled dependency relations .

Here is the basic idea: the discourse structure consists of EDUs which are linked by the binary, asymmetrical relations called dependency relations .

A dependency relation holds between a subordinate EDU called the dependent, and another EDU on

Here, both x and y are dependency relations (e.g., plmod or lobj in Figure 2).

We define the dependency structure of a dependency relation as the structure containing the dependent word (e. g., the word directly indicated by plmod, or “El?” in Figure 2) and the whole subtree under the dependency relation (all of the words that directly or indirectly depend on the dependent word, or the words under “El?” in Figure 2).

Further, we define X and Y as the corresponding dependency structures of the dependency relations x and y, respectively.

is Ragheb and Dickinson (2013), who use MASI (Passonneau, 2006) to measure agreement on dependency relations and head selection in multi-headed dependency syntax, and Bhat and Sharma (2012), who compute Cohen’s H (Cohen, 1960) on dependency relations in single-headed dependency syntax.

dency parsing: the percentage of tokens that receive the correct head and dependency relation .

When comparing syntactic trees, we only want to compare dependency relations or nonterminal categories.

Therefore we remove the leaf nodes in the case of phrase structure trees, and in the case of dependency trees we compare trees whose edges are unlabelled and nodes are labelled with the dependency relation between that word and its head; the root node receives the label 6.

The dependency tree indicates the dependency relations between words.

The dependency relation types are remained to guide the sentiment propagations in our model.

Notably, the conversion is performed recursively for the connected words and the dependency relation types are remained.

o the dependency relation nsubj (what, name) and prep_of(name, brother) indicates that the question seeks the information of a name;4

0 the dependency relation prep_of(name, brother) indicates that the name is about a brother (but we do not know whether it is a person name yet);

0 the dependency relation nn(br0ther, bieber) and the facts that, (i) Bieber is a person and (ii) a person’s brother should also be a person, indicate that the name is about a person.