Unlike previous unknown nouns tagging task (Curran, 2005) (Ciaramita and Johnson, 2003), this is the first attempt to focus on out-of- vocabulary(OOV) lexical evaluation tasks that does not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to pro- vide solutions for OOV lexical classification and predication. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we uti- lize unsupervised word embedding methods such as Word2Vec(, 2013) and Word2GM (Athiwaratkun and Wilson, 2017) to perform the baseline experiments on the cat- egorical classification task and OOV words at- tribute prediction tasks.
@cite_23 proposed supersense to classify common nouns that extends named entity classification. Based on supersense, @cite_1 improved the rule-based model by adding hand-coded unseen nouns tagging within the scope of WordNet. @cite_3 built the text simplification system closely related with WordNet to obtain the word pairs as hypernym or synonym. @cite_18 compiled the corpus based on the lexical substitution at SemEval-2007 with manually annotation. However, OOV prediction task is similar with lexical simplification whilst the difference is obvious. First, it is required to acquire prior knowledge as much as better for text simplification. Few OOV words in our task exist in WordNet, not to mention the creation of the thesaurus. With a never-seen word, the key to understand it is to familiarize with the given context. Second, most of the test words and the candidates in lexical substitution tasks such as @cite_16 are daily words. Thus, the demand for comprehension related to the whole context is less essential than ours.
@cite_18 @cite_1 @cite_3 @cite_23 @cite_16
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We describe the English Lexical Simplification task at SemEval-2012. This is the first time such a shared task has been organized and its goal is to provide a framework for the evaluation of systems for lexical simplification and foster research on context-aware lexical simplification approaches. The task requires that annotators and systems rank a number of alternative substitutes -- all deemed adequate -- for a target word in context, according to how "simple" these substitutes are. The notion of simplicity is biased towards non-native speakers of English. Out of nine participating systems, the best scoring ones combine context-dependent and context-independent information, with the strongest individual contribution given by the frequency of the substitute regardless of its context. The limited coverage of lexical-semantic resources is a significant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into WORDNET. Ciaramita and Johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but significantly outperforms their tagger. We also demonstrate the use of an extremely large shallow-parsed corpus for calculating vector-space semantic similarity. We present a method for lexical simplification. Simplification rules are learned from a comparable corpus, and the rules are applied in a context-aware fashion to input sentences. Our method is unsupervised. Furthermore, it does not require any alignment or correspondence among the complex and simple corpora. We evaluate the simplification according to three criteria: preservation of grammaticality, preservation of meaning, and degree of simplification. Results show that our method outperforms an established simplification baseline for both meaning preservation and simplification, while maintaining a high level of grammaticality. We present a new framework for classifying common nouns that extends named-entity classification. We used a fixed set of 26 semantic labels, which we called supersenses. These are the labels used by lexicographers developing WordNet. This framework has a number of practical advantages. We show how information contained in the dictionary can be used as additional training data that improves accuracy in learning new nouns. We also define a more realistic evaluation procedure than cross-validation. In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free to use any lexical resource. There is a subtask which requires identifying cases where the word is functioning as part of a multiword in the sentence and detecting what that multiword is.