First, since a sentence contributes its counts only to the translation table for the source it came from, many word pairs will be unobserved for a given table.

(2011) showed that is it beneficial to condition the lexical weighting features on provenance by assigning each sentence pair a set of features, fs(é|7), one for each domain 8, which compute a new word translation table p3(e| f) estimated from only those sentences which belong to s: 03(f, e)/Ze 03(f, e), where cs(-) is the number of occurrences of the word pair in 3.

To obtain the lexical probability conditioned on topic distribution, we first compute the expected count ezn (e, f) of a word pair under topic Zn:

where cj(-) denotes the number of occurrences of the word pair in sentence 303-, and then compute:

The dataset has three interesting characteristics: 1) human judgments are on pairs of words presented in sentential context, 2) word pairs and their contexts are chosen to reflect interesting variations in meanings of homonymous and polysemous words, and 3) verbs and adjectives are present in addition to nouns.

We obtained a total of 2,003 word pairs and their sentential contexts.

The word pairs consist of 1,712 unique words.

To capture interesting word pairs , we sample different senses of words using WordNet (Miller, 1995).

The weighting parameter a (0 S a S 1) is tuned on the MEN development data (2,000 word pairs ; details on the MEN dataset in the next section).

WordSim353 (Finkelstein et al., 2002) is a widely used benchmark constructed by asking 16 subjects to rate a set of 353 word pairs on a 10-point similarity scale and averaging the ratings (dollar/buck receives a high 9.22 average rating, professor/cucumber a low 0.31).

The version used here contained 10 judgements per word pair .

Because of its design, word pairs in MEN can be expected to be more imageable than those in WordSim, so the visual information is more relevant for this dataset.

One unique characteristic of our model is that a NE normalization variable is introduced to indicate whether a word pair belongs to the mentions of the same entity.

There are two possible ways to fix these errors: 1) Extending the scope of z-serial variables to each word pairs with a common prefix; and 2) developing advanced normalization components to restore such slang expressions and informal abbreviations into their canonical forms.

Hereafter, we use tm to denote the mth tweet ,tfn and to denote the 73th word of of tm and its BIL OU label, respectively, and If; to denote the factor related to 1 and Next, for each word pair with the same lemma, denoted by 753,, and 75%,, we introduce a binary random variable, denoted by 277%”, whose value indicates whether 75%,, and ti, belong to two mentions of the same entity.

11We first conduct a simple dictionary—lookup based normalization with the incorrect/correct word pair list provided by Han et al.