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
2026185168 2132767156 265224334 2144108169
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. 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 develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets. We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10,000 novel synsets to WordNet 2.1 at 84 precision, a relative error reduction of 70 over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23 relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.