We introduce three linguistically motivated structured regularizers based on parse trees , topics, and hierarchical word clusters for text categorization.

Figure 1: An example of a parse tree from the Stanford sentiment treebank, which annotates sentiment at the level of every constituent (indicated here by —|— and ++; no marking indicates neutral sentiment).

4.2 Parse Tree Regularizer

Sentence boundaries are a rather superficial kind of linguistic structure; syntactic parse trees provide more fine-grained information.

Instead of only using 1-best parse trees in previous work, our core idea is to utilize parse forest (ambiguous labelings) to combine multiple l-best parse trees generated from diverse parsers on unlabeled data.

1) ambiguity encoded in parse forests compromises noise in l-best parse trees .

During training, the parser is aware of these ambiguous structures, and has the flexibility to distribute probability mass to its preferred parse trees as long as the likelihood improves.

Both work employs two parsers to process the unlabeled data, and only select as extra training data sentences on which the 1-best parse trees of the two parsers are identical.

Different from traditional self/co/tri-training which only use l-best parse trees on unlabeled data, our approach adopts ambiguous labelings, represented by parse forest, as gold-standard for unlabeled sentences.

The forest is formed by two parse trees , respectively shown at the upper and lower sides of the sentence.

Figure 1 shows a constituent parse tree and its Stanford typed dependency parse tree for the same

As shown in the figure, the number of nodes in the dependency parse tree (i.e.

9) is much fewer than that in its corresponding constituent parse tree (i.e.

These pre-ordering approaches first parse the source language sentences to create parse trees .

Then, syntactic reordering rules are applied to these parse trees with the goal of reordering the source language sentences into the word order of the target language.

terrorism definition (a) A constituent parse tree

Cog-nitively, it is more plausible to assume that children obtain only terminal strings of parse trees and not the actual parse trees .

Most existing solutions treat the problem of unsupervised parsing by assuming a generative process over parse trees e.g.

Unlike in phylogenetics and graphical models, where a single latent tree is constructed for all the data, in our case, each part of speech sequence is associated with its own parse tree .

Assuming an input sentence contains n words, in order to reach a terminal state, the initial state requires n sh—x actions to consume all words in 6, and n — l rl/rr—x actions to construct a complete parse tree by consuming all the subtrees in 0.

For example, the parse tree in Figure la contains no ru—x action, while the parse tree for the same input sentence in Figure lb contains four ru—x actions.

Input: A word-segmented sentence, beam size k. Output: A constituent parse tree .

empty stack 0 and a queue 6 containing the entire input sentence (word-POS pairs), and the terminal states have an empty queue 6 and a stack 0 containing only one complete parse tree .

In order to construct lexicalized constituent parse trees , we define the following actions for the action set T according to (Sagae and Lavie, 2005; Wang et al., 2006; Zhang and Clark, 2009):

For example, in Figure l, for the input sentence wowlwg and its POS tags abc, our parser can construct two parse trees using action sequences given below these trees.

The network does not rely on a parse tree and is easily applicable to any language.

A model that adopts a more general structure provided by an external parse tree is the Recursive Neural Network (RecNN) (Pollack, 1990; Kiichler and Goller, 1996; Socher et al., 2011; Hermann and Blunsom, 2013).

It is sensitive to the order of the words in the sentence and it does not depend on external language-specific features such as dependency or constituency parse trees .

RECNTN is a recursive neural network with a tensor-based feature function, which relies on external structural features given by a parse tree and performs best among the RecNNs.

The feature graph induces a hierarchical structure somewhat akin to that in a syntactic parse tree .

The recursive neural network follows the structure of an external parse tree .

Likewise, the induced graph structure in a DCNN is more general than a parse tree in that it is not limited to syntactically dictated phrases; the graph structure can capture short or long-range semantic relations between words that do not necessarily correspond to the syntactic relations in a parse tree .

The DCNN has internal input-dependent structure and does not rely on externally provided parse trees , which makes the DCNN directly applicable to hard-to-parse sentences such as tweets and to sentences from any language.

To obtain syntactic parse trees and semantic roles on the tuning and test datasets, we first parse the source sentences with the Berkeley Parser (Petrov and Klein, 2007), trained on the Chinese Treebank 7.0 (Xue et al., 2005).

In order to understand how well the MR08 system respects their reordering preference, we use the gold alignment dataset LDC2006E86, in which the source sentences are from the Chinese Treebank, and thus both the gold parse trees and gold predicate-argument structures are available.

(2012) obtained word order by using a reranking approach to reposition nodes in syntactic parse trees .

According to the annotation principles in (Chinese) PropB ank (Palmer et al., 2005; Xue and Palmer, 2009), all the roles in a PAS map to a corresponding constituent in the parse tree , and these constituents (e.g., NPs and VBD in Figure 1) do not overlap with each other.

parse tree and its word alignment links to the target language.

Given a hypothesis H with its alignment a, it traverses all CFG rules in the parse tree and sees if two adjacent constituents are conditioned to trigger the reordering models (lines 2-4).

We split the sentence based on the ending punctuation, predict the parse tree for each segment and group the roots of resulting trees into a single node.

Because the number of alternatives is small, the scoring function could in principle involve arbitrary (global) features of parse trees .

Reranking can be combined with an arbitrary scoring function, and thus can easily incorporate global features over the entire parse tree .

Ideally, we would change multiple heads in the parse tree simultaneously, and sample those choices from the corresponding conditional distribution of p. While in general this is increasingly difficult with more heads, it is indeed tractable if

3/ is always a valid parse tree if we allow multiple children of the root and do not impose projective constraint.

We extend our model such that it jointly learns how to predict a parse tree and also correct the predicted POS tags for a better parsing performance.

Data As described earlier, the Stanford Sentiment Treebank (Socher et al., 2013) has manually annotated, real-valued sentiment values for all phrases in parse trees .

The recently available Stanford Sentiment Treebank (Socher et al., 2013) renders manually annotated, real-valued sentiment scores for all phrases in parse trees .

Such models work in a bottom-up fashion over the parse tree of a sentence to infer the sentiment label of the sentence as a composition of the sentiment expressed by its constituting parts.

A recursive neural tensor network (RNTN) is a specific form of feed-forward neural network based on syntactic (phrasal-structure) parse tree to conduct compositional sentiment analysis.

Each node of the parse tree is a fixed-length vector that encodes compositional semantics and syntax, which can be used to predict the sentiment of this node.

The constituent parse trees were then transformed into dependency parse trees , using the head of each constituent (Jiang and Zhai, 2007b).

We extract features from a sequence representation and a parse tree representation of each relation instance.

Syntactic Features The syntactic parse tree of the relation instance sentence can be augmented to represent the relation instance.

Each node in the sequence or the parse tree is augmented by an argument tag that indicates whether the node corresponds to entity A, B, both, or neither.

To extract a Feature-Based Lexicalised Tree Adjoining Grammar (FB-LTAG) from the KB Gen data, we parse the sentences of the training corpus; project the entity and event variables to the syntactic projection of the strings they are aligned with; and extract the elementary trees of the resulting FB-LTAG from the parse tree using semantic information.

After alignment, the entity and event variables occurring in the input semantics are associated with substrings of the yield of the syntactic parse tree .

Once entity and event variables have been projected up the parse trees , we extract elementary FB-LTAG trees and their semantics from the input scenario as follows.

Figure l: A CCG parse tree for the mention “one week ago.” The tree includes forward (>) and backward (<) application, as well as two type-shifting operations

The lexicon pairs words with categories and the combinators define how to combine categories to create complete parse trees .

For example, Figure 1 shows a CCG parse tree for the phrase “one week ago.” The parse tree is read top to bottom, starting from assigning categories to words using the lexicon.

Model Let y be a context-dependent CCG parse, which includes a parse tree TR(y), a set of context operations CNTX(y) applied to the logical form at the root of the tree, a final context-dependent logical form LF(y) and a TIMEX3 value Define gb(m, D, 3/) 6 Rd to be a d-dimensional feature—vector representation and 6 6 Rd to be a parameter vector.

First, it is semantic based in that it takes as input a deep semantic representation rather than e.g., a sentence or a parse tree .

While previous simplification approaches starts from either the input sentence or its parse tree , our model takes as input a deep semantic representation namely, the Discourse Representation Structure (DRS, (Kamp, 1981)) assigned by Boxer (Curran et al., 2007) to the input complex sentence.

Their simplification model encodes the probabilities for four rewriting operations on the parse tree of an input sentences namely, substitution, reordering, splitting and deletion.

(2010) and the edit history of Simple Wikipedia, Woodsend and Lapata (2011) learn a quasi synchronous grammar (Smith and Eisner, 2006) describing a loose alignment between parse trees of complex and of simple sentences.

Figure 2: A parse tree (left) and its descending paths according to Definition 1 (l - length).

Definition 1 (Descending Path): Let T be a parse tree , 2) any nonterminal node in T, do a descendant of 2), including terminals.

Figure 2 illustrates a parse tree and its descending paths of different lengths.

Many of them focus on using tree kernels to learn parse tree structure related features (Collins and Duffy, 2001; Culotta and Sorensen, 2004; Bunescu and Mooney, 2005).

Figure 2: A Parse Tree Example

Consider the sentence: “Antibiotics are the standard therapy for Lyme disease”: MedicalESG first generates a dependency parse tree (Figure 2) to represent grammatical relations between the words in the sentence, and then associates the words with CUIs.

The input to the algorithm are a parse tree T and a set of target entities E. We first generate combinations of 1-3 elements of E (line 10), then for each combination 0 we identify all the nodes in T that mention any of the entities in C. We continue by constructing the MST of these nodes, and finally apply our heuristics to the nodes in the MST.

We highlighted in bold the path corresponding to the linearized form (b) of the example parse tree (a).

Instead, we chose to modify the method of Filippova and Altun (2013) because it relies on dependency parse trees and does not use any LM scoring.

Figure 1: An example of compositionality in ideological bias detection (red —> conservative, blue —> liberal, gray —> neutral) in which modifier phrases and punctuation cause polarity switches at higher levels of the parse tree .

Based on a parse tree , these words form phrases p (Figure 2).

The increased accuracy suggests that the trained RNNs are capable of detecting bias polarity switches at higher levels in parse trees .

Most prior work on learning compositional semantic representations employs parse trees on their training data to structure their composition functions (Socher et al., 2012; Hermann and Blunsom, 2013, inter alia).

While these methods have been shown to work in some cases, the need for parse trees and annotated data limits such approaches to resource-fortunate languages.

This removes a number of constraints that normally come with CVM models, such as the need for syntactic parse trees , word alignment or annotated data as a training signal.

We first design two discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory.

First, we defined two simple discourse-aware similarity metrics (lexicalized and un-lexicalized), which use the all-subtree kernel to compute similarity between discourse parse trees in accordance with the Rhetorical Structure Theory.

Combination of four metrics based on syntactic information from constituency and dependency parse trees : ‘CP—STM-4’, ‘DP-HWCM_c-4’, ‘DP—HWCM1-4’, and ‘DP-Or(*)’.