14,807 research outputs found
Embedding Semantic Relations into Word Representations
Learning representations for semantic relations is important for various
tasks such as analogy detection, relational search, and relation
classification. Although there have been several proposals for learning
representations for individual words, learning word representations that
explicitly capture the semantic relations between words remains under
developed. We propose an unsupervised method for learning vector
representations for words such that the learnt representations are sensitive to
the semantic relations that exist between two words. First, we extract lexical
patterns from the co-occurrence contexts of two words in a corpus to represent
the semantic relations that exist between those two words. Second, we represent
a lexical pattern as the weighted sum of the representations of the words that
co-occur with that lexical pattern. Third, we train a binary classifier to
detect relationally similar vs. non-similar lexical pattern pairs. The proposed
method is unsupervised in the sense that the lexical pattern pairs we use as
train data are automatically sampled from a corpus, without requiring any
manual intervention. Our proposed method statistically significantly
outperforms the current state-of-the-art word representations on three
benchmark datasets for proportional analogy detection, demonstrating its
ability to accurately capture the semantic relations among words.Comment: International Joint Conferences in AI (IJCAI) 201
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Distinguishing between antonyms and synonyms is a key task to achieve high
performance in NLP systems. While they are notoriously difficult to distinguish
by distributional co-occurrence models, pattern-based methods have proven
effective to differentiate between the relations. In this paper, we present a
novel neural network model AntSynNET that exploits lexico-syntactic patterns
from syntactic parse trees. In addition to the lexical and syntactic
information, we successfully integrate the distance between the related words
along the syntactic path as a new pattern feature. The results from
classification experiments show that AntSynNET improves the performance over
prior pattern-based methods.Comment: EACL 2017, 10 page
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