To find the stable properties that can underpin a meaningful fine-grained category for cowboy, we must seek out the properties that are so often presupposed to be salient of all cowboys that one can use them to anchor a simile, such as "swaggering like a cowboy” or “as grizzled as a cowboy”.

Since each hit will also yield a value for S via the wildcard *, and a fine-grained category PS for C, we use this approach here to harvest fine-grained categories from the web from most of our similes.

After 2 cycles we acquire 43 categories; after 3 cycles, 72; after 4 cycles, 93; and after 5 cycles, we acquire 102 fine-grained perspectives on cola, such as stimu-lating-drink and corrosive-substance.

We also want any fine-grained perspective M-H to influence our similarity metric, provided it can be coherently tied into WordNet as a shared hypemym of the two lexical concepts being compared.

The denominator in (2) denotes the sum total of the size of all fine-grained categories that can be coherently added to WordNet for any term.

For a shared dimension H in the feature vectors of concepts C1 and C2, if at least one fine-grained perspective M-H has been added to WordNet between H and C1 and between H and C2, then the value of dimension H for C1 and for C2 is given by (4):

A fine-grained category hierarchy permits fine-grained similarity judgments, and though WordNet is useful, its sense hierarchies are not especially fine-grained .

However, we can automatically make WordNet subtler and more discerning, by adding new fine-grained categories to unite lexical concepts whose similarity is not reflected by any existing categories.

Veale (2003) shows how a property that is found in the glosses of two lexical concepts, of the same depth, can be combined with their LCS to yield a new fine-grained parent category, so e.g.

Also, fine-grained sense and frame distinctions may be more relevant in one language than in another language.

We however find lower performance for verbs in a fine-grained setting.

We argue that an improved alignment algorithm, for instance taking subcategorization information into account, can identify the fine-grained distinctions.

The verb senses are very fine-grained and thus present a difficult alignment task.

A number of false positives occur because the gold standard was developed in a very fine-grained manner: distinctions such as causative vs. inchoa-tive (enlarge: become large vs. enlarge: make large) were explicitly stressed in the definitions and thus annotated as different senses by the annotators.

Because sense granularity was an issue in the error analysis, we considered two alignment decisions: (a) fine-grained alignment: the two glosses describe the same sense; (b) coarse-grained alignment.

The precision for the fine-grained (a) is lower than the allover precision on the gold standard.

HYENA (Hierarchical tYpe classification for Entity NAmes), the method of (Yosef 2012), based on a feature-rich classifier for fine-grained , hierarchical type tagging.

Our aim is for all recognized and newly discovered entities to be semantically interpretable by having fine-grained types that connect them to KB classes.

For informative knowledge, new entities must be typed in a fine-grained manner (e.g., guitar player, blues band, concert, as opposed to crude types like person, organization, event).

Therefore, our setting resembles the established task of fine-grained typing for noun phrases (Fleis-chmann 2002), with the difference being that we disregard common nouns and phrases for prominent in-KB entities and instead exclusively focus on the difficult case of phrases that likely denote new entities.

There is fairly little work on fine-grained typing, notable results being (Fleischmann 2002; Rahman 2010; Ling 2012; Yosef 2012).

These methods consider type taxonomies similar to the one used for PEARL, consisting of several hundreds of fine-grained types.

This paper addresses the task of fine-grained opinion extraction — the identification of opinion-related entities: the opinion expressions, the opinion holders, and the targets of the opinions, and the relations between opinion expressions and their targets and holders.

For evaluation, we used version 2.0 of the MPQA corpus (Wiebe et al., 2005; Wilson, 2008), a widely used data set for fine-grained opinion analysis.6 We considered the subset of 482 documents7 that contain attitude and target annotations.

Fine-grained opinion analysis is concerned with identifying opinions in text at the expression level; this includes identifying the subjective (i.e., opinion) expression itself, the opinion holder and the target of the opinion (Wiebe et al., 2005).

Not surprisingly, fine-grained opinion extraction is a challenging task due to the complexity and variety of the language used to express opinions and their components (Pang and Lee, 2008).

We evaluate our approach using a standard corpus for fine-grained opinion analysis (the MPQA corpus (Wiebe et al., 2005)) and demonstrate that our model outperforms by a significant margin traditional baselines that do not employ joint inference for extracting opinion entities and different types of opinion relations.

Significant research effort has been invested into fine-grained opinion extraction for open-domain text such as news articles (Wiebe et al., 2005; Wilson et al., 2009).

This fine-grained scale resulted in higher mean pairwise inter-rater correlations than the traditional integer-only scale (r=0.79 vs around r=0.70 for the operational scoring).

This dataset provides a very fine-grained ranking of the essays, with almost no two essays getting exactly the same score.

This is a very competitive baseline, as e-rater features explain more than 70% of the variation in essay scores on a relatively coarse scale (setA) and more than 80% of the variation in scores on a fine-grained scale (setB).

We chose a relatively fine-grained binning and performed no optimization for grid selection; for more sophisticated gridding approaches to study nonlinear relationships in the data, see Reshef et al.

Note that the information gain agent starts dialogs with the top-level and appropriate subcategory questions, so it is only for longer dialogs that the fine-grained aspects boost performance.

To identify these subcategories, we run Latent Dirichlet Analysis (LDA) (Blei et al., 2003) on the reviews of each set of businesses in the twenty most common top-level categories, using 10 topics and concatenating all of a business’s reviews into one document.2 Several researchers have used sentence-level documents to model topics in reviews, but these tend to generate topics about fine-grained aspects of the sort we discuss in Section 2.2 (Jo and Oh, 2011; Brody and Elhadad, 2010).

2.2 Questions from Fine-Grained Aspects

The framework makes use of techniques from topic modeling and sentiment-based aspect extraction to identify fine-grained attributes for each business.

However, recent work has shown that parsing results can be greatly improved by defining more fine-grained syntactic

This gives a fine-grained notion of semantic similarity, which is useful for tackling problems like ambiguous attachment decisions.

The former can capture the discrete categorization of phrases into NP or PP while the latter can capture fine-grained syntactic and compositional-semantic information on phrases and words.

This mainly consisted in relabeling dependency relations and, due to the fine-grained label set used in the Swedish Treebank (Teleman, 1974), this could be done with high precision.

Making fine-grained label distinctions was discouraged.

Such a reduction may ultimately be necessary also in the case of dependency relations, but since most of our data sets were created through manual annotation, we could afford to retain a fine-grained analysis, knowing that it is always possible to map from finer to coarser distinctions, but not vice versa.4

In our approach, we classify sentences as S-(non)relevant because this is the most fine-grained level at which S-relevance manifests itself; at the word or phrase level, S-relevance classification is not possible because of scope and context effects.

Our work is most closely related to (Taboada et al., 2009) who define a fine-grained classification that is similar to sentiment relevance on the highest level.

Tackstro'm and McDonald (2011) develop a fine-grained annotation scheme that includes S-nonrelevance as one of five categories.