Relation Extraction (RE) is the task of extracting semantic relationships between entities in text.

Recent studies on relation extraction are mostly supervised.

In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic analysis (LSA) and (ii) structured kernels to improve the adaptability of relation extractors to new text genres/domains.

(2006), including gold-standard information on entity and mention type substantially improves relation extraction performance.

We treat relation extraction as a multi-class classification problem and use SVM-light-TK4 to train the binary classifiers.

Relation extraction is the task of extracting semantic relationships between entities in text, e.g.

Recent studies on relation extraction have shown that supervised approaches based on either feature or kernel methods achieve state-of-the-art accuracy (Ze-lenko et al., 2002; Culotta and Sorensen, 2004;

However, to the best of our knowledge, there is almost no work on adapting relation extraction (RE) systems to new domains.1 There are some prior studies on the related tasks of malti-task transfer learning (Xu et al., 2008; Jiang, 2009) and distant supervision (Mintz et al., 2009), which are clearly related but different: the former is the problem of how to transfer knowledge from old to new relation types, while distant supervision tries to learn new relations from unlabeled text by exploiting weak-supervision in the form of a knowledge resource (e.g.

Thus, we present a novel application of semantic syntactic tree kernels and Brown clusters for domain adaptation of tree-kernel based relation extraction .

We adopted the evaluation metrics for entity and relation extraction from Choi et al.

We trained the classifiers for relation extraction using L1-regu1arized logistic regression with default parameters using the LIBLINEAR (Fan et al., 2008) package.

Three relation extraction techniques were used in the baselines:

2007; Yang and Cardie, 2012)) and relation extraction techniques have been proposed to extract opinion holders and targets based on their linking relations to the opinion expressions (e. g. Kim and Hovy (2006), Kobayashi et al.

We model entity identification as a sequence tagging problem and relation extraction as binary classification.

In this section, we will describe how we model opinion entity identification and opinion relation extraction , and how we combine them in a joint inference model.

3.2 Opinion Relation Extraction

In the following we will not distinguish these two relations, since they can both be characterized as relations between opinion expressions and opinion arguments, and the methods for relation extraction are the same.

3.1 Comparable Relation Extraction

An important observation for the task of comparable relation extraction is that many relations are complex multiword expressions, and thus their automatic detection is not trivial.

ger (Lafferty et al., 2001) to the task, since CRF was shown to be state-of-the-art for sequential relation extraction (Mooney and Bunescu, 2005; Culotta et al., 2006; J indal and Liu, 2006).

Our extraction of comparable relations falls within the field of Relation Extraction , in which CRF is a state-of-the-art method (Mooney and Bunescu, 2005; Culotta et al., 2006).

We also leverage synonym-matching techniques for comparing relations extracted from text with Freebase relations.

Our techniques for comparing relations fit into this line of work, but they are novel in their application of these techniques to the task of comparing database relations and relations extracted from text.

Schema matching in the database sense often considers complex matches between relations (Dhamanka et al., 2004), whereas as our techniques are currently restricted to matches involving one database relation and one relation extracted from text.

In order to deduce the speaker of the utterance, we need to combine the three pieces of information: (a) the utterance is addressed to Lizzy (vocative prediction), (b) the utterance is produced by Lizzy’s father (pronoun resolution), and (c) Mr. Bennet is the father of Lizzy ( relationship extraction ).

A joint approach to resolving speaker attribution, relationship extraction , co-reference resolution, and alias-to-character mapping would not only improve the accuracy on all these tasks, but also represent a step towards deeper understanding of complex plots and stories.

A preliminary manual inspection of the set of relations extracted by this method (Makazhanov et al., 2012) indicates that all of them are correct, and include about 40% all personal relations that can be inferred by a human reader from the text of the novel.