The final feature vector is the concatenation of structural features gbs(w,z), which consider the selected nodes in the DOM tree, and denotation features gbd(:c, y), which look at the extracted entities.

One main focus of our work is finding good feature representations for a list of objects (DOM tree nodes for structural features and entity strings for denotation features).

Structural feature Value Features on selected nodes:

Setting Acc A@5 All features 41.1 :I: 3.4 58.4 :I: 2.7 Oracle 68.7 :I: 2.4 68.7 :I: 2.4 (Section 4.5) Structural features only 36.2 :I: 1.9 54.5 :I: 2.5 Denotation features only 19.8 :I: 2.5 41.7 :I: 2.7 (Section 4.6) Structural + query-denotation 41.7 :I: 2.5 58.1 :I: 2.4 Query-denotation features only 25.0 :I: 2.3 48.0 :I: 2.7 Concat.

We observe that denotation features improves accuracy on top of structural features .

On the other hand, structural features prevent the system from selecting random entities outside the main part of the page.

To generalize across different inputs, we rely on two types of features: structural features , which look at the layout and placement of the entities being extracted; and denotation fea-

Therefore, it runs in linear time and can take advantage of arbitrarily complex structural features from already constructed subtrees.

Third, transition-based parsers have the freedom to define arbitrarily complex structural features, but this freedom has not fully been taken advantage of and most of the present approaches only use simple structural features .

Third, we take into account two groups of complex structural features that have not been previously used in transition-based parsing: nonlocal features (Charniak and Johnson, 2005) and semi-supervised word cluster features (Koo et al., 2008).

One advantage of transition-based constituent parsing is that it is capable of incorporating arbitrarily complex structural features from the already constructed subtrees in 0 and unprocessed words in 6.

However, all the feature templates given in Table l are just some simple structural features .

To further improve the performance of our transition-based constituent parser, we consider two group of complex structural features : nonlocal features (Chamiak and Johnson, 2005; Collins and Koo, 2005) and semi-supervised word cluster features (Koo et al., 2008).

The reason is that the single-stage chart-based parser cannot use nonlocal structural features .

In contrast, the transition-based parser can use arbitrarily complex structural features .

Our technique is designed to not rely much on structural features such as post metadata since such features are often not uniformly available across forums.

We show that our technique is able to effectively identify solutions using just one non-content based feature, the post position, whereas previous techniques in literature have depended heavily on structural features (that are not always available in many forums) and supervised information.

Thus, our technique is able to exploit any extra solution identifying structural features that are available.

Though such assumptions on structural features, if generic enough, may be built into unsupervised techniques to aid solution identification, the variation in availability of such features across forums limits the usage of models that rely heavily on structural features .

In particular, we show that by using post position as the only non-textual feature, we are able to achieve accuracies comparable to supervision-based approaches that use many structural features (Catherine et al., 2013).

Towards this, we make use of a structural feature ; in particular, adapting the hypothesis that solutions occur in the first N posts (Ref.

We will show that we are able to effectively perform solution identification using our approach by exploiting just one structural feature , the post position, as above.

We propose a new set of structural features .

In order to mitigate this issue, we use an indicator feature for each structural feature to denote whether or not it is valid.

The performance of the system using each structural feature class on its own is very low.

Perplexingly, adding all structural features to LEX reduces the accuracy by around 2.2 percentage points.

The structural model, in contrast, is able to identify the product of interest (xoom) and associate it with the negative expression through a structural feature and thus correctly classify the comment as negat ive.

In contrast, we show that adding structural features from syntactic trees is particularly useful for the cross-domain setting.

These trees are input to tree kernel functions for generating structural features .