Learning Discriminative Projections for Text Similarity Measures. Wen-tau Yih, Kristina Toutanova, John C. Platt, Christopher Meek. CoNLL 2011

• Claims:
• We propose a new projection learning framework, Similarity Learning via Siamese Neural Network (S2Net), to discriminatively learn the concept vector representations of input text objects.
• Comment:
• Input is pairs of words that are known to be similar/dissimilar.

Web-Scale Distributional Similarity and Entity Set Expansion. Patrick Pantel, Eric Crestan, Arkady Borkovsky, Ana-Maria Popescu, Vishnu Vyas. EMNLP 2009

• Claims
• propose an algorithm for large-scale term similarity computation
• Comments
• Lists applications of semantic similarity: word classification, word sense disambiguation, context-spelling correction, fact extraction, semantic role labeling, query expansion, textual advertising
• they apply the learned similarity matrix to the task of automatic
  set expansion

Corpus-based Semantic Class Mining: Distributional vs. Pattern-Based Approaches. Shuming Shi, Huibin Zhang, Xiaojie Yuan, Ji-Rong Wen. COLING 2010

• Claims
• perform an empirical comparison of [previous research work] [on semantic class mining]
• propose a frequency-based rule to select appropriate approaches for different types of terms.
• Comments
• Jargon: semantically similar words are also called "peer terms or coordinate terms".
• States that "DS [distributional similarity] approaches basically exploit second-order co-occurrences to discover strongly associated concepts." How is that?
• Extrinsic evaluation by set expansion

A Mixture Model with Sharing for Lexical Semantics. Joseph Reisinger, Raymond Mooney. EMNLP 2010

• Claims
• Multi-prototype representations [are good for] words with several unrelated meanings (e.g. bat and club), but are not suitable for representing the common ... structure [shared across senses] found in highly polysemous words such as line or run. We introduce a mixture model for capturing this---mixture of a Dirichlet Process clustering model and a background model.
• we derive a multi-prototype representation capable of capturing varying degrees of sharing between word senses, and demonstrate its effectiveness in the word-relatedness task in the presence of highly polysemous words.
• Comments
• Positions lexical semantics as the umbrella task with subtasks such as word relatedness and selectional preferences