Out of these, 4,011 are positive relation examples annotated with 6 coarse-grained relation types and 22 fine-grained relation types5 .

We similarly build a fine-grained classifier to disambiguate between 45 relation labels.

We built one binary, one coarse-grained, and one fine-grained classifier for each fold.

The second measures the similarity between the source query and each target query, and then combines these fine-grained similarity values for its importance estimation.

The second measures the similarity between a source query and each target query, and then combine the fine-grained similarity values to estimate its importance to target domain.

By contrast, more accurate query weights can be achieved by the more fine-grained similarity measure between the source query and all target queries in algorithm 2.

fine-grained similarity values.

more precise measures of query similarity by utilizing the more fine-grained classification hyperplane for separating the queries of two domains.

The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet.

Following the two-stage prediction aggregation methods (Krishnan and Manning, 2006), such pre-labeled results, together with other conventional features used by the state-of-the-art NER systems, are fed into a linear Conditional Random Fields (CRF) (Lafferty et al., 2001) model, which conducts fine-grained tweet level NER.

Our model is hybrid in the sense that a KNN classifier and a CRF model are sequentially applied to the target tweet, with the goal that the KNN classifier captures global coarse evidence while the CRF model fine-grained information encoded in a single tweet and in the gazetteers.

model, which is good at encoding the subtle interactions between words and their labels, compensates for KNN’s incapability to capture fine-grained evidence involving multiple decision points.

Specifically, we are interested in identifying fine-grained product properties across reviews (e.g., battery life for electronics or pizza for restaurants) as well as capturing attributes of these properties, namely aggregate user sentiment.

Property: A property corresponds to some fine-grained aspect of a product.

While our model captures similar high-level intuition, it analyzes fine-grained properties expressed at the snippet level, rather than document-level sentiment.