We use part-of-speech unigrams and bigrams in order to capture MWEs with irregular syntactic structures that might indicate the id-iomacity of a word sequence.

We also integrated mixed bigrams made up of a word and a part-of-speech .

We associate each word with its part-of-speech tags found in our external morphological lexicon.

In this paper, we focus on contiguous MWEs that form a lexical unit which can be marked by a part-of-speech tag (e. g. at night is an adverb, because of is a preposition).

Compounds are identified with a specific nonterminal symbol ”MWX” where X is the part-of-speech of the expression.

They have a flat structure made of the part-of-speech of their components as shown in figure 1.

The nonterminal tagset is composed of 14 part-of-speech labels and 24 phrasal ones (including 11 MWE labels).

Constant and Sigogne (2011) proposed to combine MWE segmentation and part-of-speech tagging into a single sequence labelling task by assigning to each token a tag of the form TAG+X where TAG is the part-of-speech (POS) of the leXical unit the token belongs to and X is either B (i.e.

Interestingly, this distinctiveness takes place at the level of words, but not at the level of other syntactic features: the part-of-speech composition of memorable quotes is in fact more likely with respect to newswire.

Thus, we can think of memorable quotes as consisting, in an aggregate sense, of unusual word choices built on a scaffolding of common part-of-speech patterns.

In particular, we analyze a corpus of advertising slogans, and we show that these slogans have significantly greater likelihood at both the word level and the part-of-speech level with respect to a language model trained on memorable movie quotes, compared to a corresponding language model trained on non-memorable movie quotes.

We then develop models using features based on the measures formulated earlier in this section: generality measures (the four listed in Table 4); distinctiveness measures (likelihood according to l, 2, and 3-gram “common language” models at the lexical and part-of-speech level for each quote in the pair, their differences, and pairwise comparisons between them); and similarity-to-slogans measures (likelihood according to l, 2, and 3-gram slogan-language models at the lexical and part-of-speech level for each quote in the pair, their differences, and pairwise comparisons between them).

We first perform word segmentation (if needed) and part-of-speech tagging.

After that, we obtain the word-segmented sentences with the part-of-speech tags.

The feature templates are outlined in Table l, where TYPE refers to one of the typeszPL or PR, h_pos refers to the part-of-speech tag of :1: h, h_word refers to the lexical form of :1: h, ch_pos refers to the part-of-speech tag of mch, and ch_word refers to the lexical form of mm.