The state-of—the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arc-factored model and the large—margin learning techniques.

Here we follow the arc factored method and define the score of a dependency tree as the sum of the scores of all the arcs in the tree.

Thus, the optimal dependency tree for T is a spanning tree with the highest score and obtained through the function DT(T,w): DT(T, w) = argmaxGT gVXRMO score(T, GT)

and maximum spanning tree (MST) algorithm are used respectively to parse the optimal projective and non-projective dependency trees with the large-margin learning technique (Crammer and Singer, 2003).

According to the definition, we illustrate all the 9 possible unlabeled dependency trees for a text containing three EDUs in Figure 2.

The dependency trees 1’ to 7’ are projective while 8’ and 9’ are non-projective with crossing arcs.

Since dependency trees contain much fewer nodes and on average they are simpler than constituency based trees, the current dependency parsers can have a relatively low computational complexity.

In our work, we adopt the graph based dependency parsing techniques learned from large sets of annotated dependency trees .

In this paper, we investigate the problem of character-level Chinese dependency parsing, building dependency trees over characters.

In this formulation, a character-level dependency tree satisfies the same constraints as the traditional word-based dependency tree for Chinese, including proj ectiVity.

The character-level dependency trees hold to a specific word segmentation standard, but are not limited to it.

Chinese dependency trees were conventionally defined over words (Chang et al., 2009; Li et al., 2012), requiring word segmentation and POS-tagging as preprocessing steps.

Such annotations enable dependency parsing on the character level, building dependency trees over Chinese characters.

(a) a word-based dependency tree

Given an input sentence x = wowl...wn, the goal of dependency parsing is to build a dependency tree as depicted in Figure 1, denoted by d = {(h,m) :0 S h S 71,0 < m S n},where (h,m) indicates a directed arc from the head word 21);, to the modifier mm, and we is an artificial node linking to the root of the sentence.

The graph-based method views the problem as finding an optimal tree from a fully-connected directed graph (McDonald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007; K00 and Collins, 2010), while the transition-based method tries to find a highest-scoring transition sequence that leads to a legal dependency tree (Yamada and Matsumoto, 2003; Nivre, 2003; Zhang and Nivre, 2011).

In this work, we adopt the graph-based paradigm because it allows us to naturally derive conditional probability of a dependency tree (1 given a sentence X, which is required to compute likelihood of both labeled and unlabeled data.

For a given tweet, we first convert its dependency tree for the interested target.

SVM-conn: The words, punctuations, emoti-cons, and #hashtags included in the converted dependency tree are used as the features for SVM.

RNN: It is performed on the converted dependency tree Without adaptive composition selection.

RNN is also based on the converted dependency tree .

The dependency tree indicates the dependency relations between words.

Algorithm 1 Convert Dependency Tree Input: Target node, Dependency tree Output: Converted tree 1: function CONV(7“) 2: E. <— SORT(dep edges connected with 7“) v <— 7“ for (7“ A 77/77 A 7“) in Er do if 7“ is head of u then 712 <— node with CONV(u), v as children else 712 <— node with v, CONV(u) as children

As illustrated in the Algorithm 1, we recursively convert the dependency tree starting from the target node.

It contains projective dependency trees that were automatically derived from the latest status of the French Treebank (Abeille and Barrier, 2004), which consists of constituency trees for sentences from the

Figure 1: French dependency tree for L’abus de biens sociaux fut de’nonce’ en vain (literally the misuse of assets social was denounced in vain, meaning The misuse of corporate assets was denounced in vain), containing two MWEs (in red).

In the dependency trees , there is no “node” for a MWE as a whole, but one node per MWE component (more generally one node per token).

While the realistic scenario of syntactic parsing with automatic MWE recognition (either done jointly or in a pipeline) has already been investigated in constituency parsing (Green et al., 2011; Constant et al., 2012; Green et al., 2013), the French dataset of the SPMRL 2013 Shared Task (Seddah et al., 2013) only recently provided the opportunity to evaluate this scenario within the framework of dependency syntax.2 In such a scenario, a system predicts dependency trees with marked groupings of tokens into MWEs.

The trees show syntactic dependencies between semantically sound units (made of one or several tokens), and are thus particularly appealing for downstream semantic-oriented applications, as dependency trees are considered to be closer to predicate-argument structures.

To our knowledge, the first works3 on predicting both MWEs and dependency trees are those presented to the SPMRL 2013 Shared Task that provided scores for French (which is the only dataset containing MWEs).

Figure 1: An example dependency tree .

Dependency trees represent syntactic relationships through labeled directed edges between heads and their dependents.

For example, Figure 1 shows a dependency tree for the sentence, Economic news had little efi‘ect on financial markets, with the sentence’s root-symbol as its root.

In this paper, we will use the following notation: :13 represents a generic input sentence, and y represents a generic dependency tree .

Fortunately we can simply convert DEP-DTs to obtain dependency trees between sentences.

After the document tree is obtained, we use a dependency parser to obtain the syntactic dependency trees of sentences.

We added two types of constraints to our model to extract a grammatical sentence subtree from a dependency tree:

recently transformed RST trees into dependency trees and used them for single document summarization (Hirao et al., 2013).

They formulated the summarization problem as a tree knapsack problem with constraints represented by the dependency trees .

Extracting a subtree from the dependency tree of words is one approach to sentence compression (Tomita et al., 2009; Qian and Liu, 2013; Morita et al., 2013; Gillick and Favre, 2009).

Our work stands in between traditional dependency tree parsing and deep linguistic processing.

There are two differences of the head word passing between our GR extraction and a “normal” dependency tree extraction.

These measures correspond to attachment scores (LASflJAS) in dependency tree parsing.

graphs than syntactic dependency trees .

Transition-based parsers utilize transition systems to derive dependency trees together with treebank-induced statistical models for predicting transitions.

Developing features has been shown crucial to advancing the state-of-the-art in dependency tree parsing (Koo and Collins, 2010; Zhang and Nivre, 2011).

To this end, we build a trie of dependency trees (which we call a tree-trie) by scanning all the dependency parses in the news training

Algorithm 2 STORE(T, I): store the dependency tree T in the tree-trie I. : /* Entry p0int/* L <— T.LINEARIZE() STORERECURSION(I.R00T(), L, 0) return M /* Procedures /* : procedure STORERECURSION(n, L, 0) ifo 2: L.LENGTH() then n.ADDTREESTRUCTURE(L.STRUCTURE()) return 10: if not n.HAsCHILD(L.TOKEN(o)) then 11: n.ADDCHILD(L.TOKEN(o))

First, each dependency tree (a) is linearized, resulting in a data structure that consists of two aligned sequences (b).

1) is formulated over weighted edges in a transformed dependency tree and is subject to a number of constraints.

(2012), adapted to dependency trees .

For dependency trees , the input corpus is permuted as follows:

For example in the trees in figure 2, assigning any other head than the root to the PRED nodes directly dominated by the root will result in invalid (cyclic and unconnected) dependency trees .

Figure 1: Transformation of dependency trees before comparison

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.

contain non-projective dependency trees .

We denote sentences by ac and the corresponding dependency trees by y E 3?

is the set of valid (projective or non-projective) dependency trees for sentence cc.

The decoding problem consists of finding a valid dependency tree y 6 32(53) that maximizes the score s(:c,y) = 6 - f (:c,y) with parameters 6.

Let a(y) E {0, 1}” denote the incidence vector of tokens contained in the n-gram sequence y and ,6(z) E {0, 1}” denote the incidence vector of words contained in the dependency tree 2.

Linear constraints are introduced to produce dependency structures that are close to the optimal dependency trees .

In order to avoid cycles in the dependency tree , we include additional variables to establish single-commodity flow (Magnanti and Wolsey, 1994) between all pairs of tokens.

Since our system uses an off-the-shelf dependency parser, and semantic representations are obtained from simple rule-based conversion from dependency trees , there will be only one (right or wrong) interpretation in face of ambiguous sentences.

DCS trees has been proposed to represent natural language semantics with a structure similar to dependency trees (Liang et al., 2011) (Figure 1).

We obtain DCS trees from dependency trees , to bypass the need of a concrete database.