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Publications (56)

1Distributional Similarity Models: Clustering vs. Nearest Neighbors1999  Lillian LeeDistributional Similarity Models: Clustering vs . Nearest Neighbors Li l l ian Lee Depar tment of Computer Science...

2PartOfSpeech Induction From Scratch1993  Hinrich SchutzeTable 2 shows 20 randomly selected words and their nearest neighbors in category space (in order of proximity to the head...

3Improving Word Representations via Global Context and Multiple Word Prototypesfirst present a qualitative analysis comparing the nearest neighbors of our model’s embeddings with those of others, showing...

4Automatic Retrieval and Clustering of Similar Words1998  Dekang Linplanned 0.06 .... Two words are a pair of respective nearest neighbors (RNNs) if each is the other's most similar word. Our...

5Semisupervised condensed nearest neighbor for partofspeech tagging2011  Anders Søgaard(WCNN) algorithm is sketched in Figure 2. C inspects k nearest neighbors when labeling new data points, where k is estimated...

6Detecting Metaphor by Contextual Analogy2013  Eirini Florouparsed data to find distributionally similar words (nearest neighbors) to the target word which will reflect the different...

7MemoryBased Learning: Using Similarity for Smoothing1997  Jakub Zavrel,Walter Daelemans"correct" output. When a new pattern is processed, the k nearest neighbors of the pattern are retrieved from memory using some...

8Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales2005  Boyeong Kang,Lillian Leemetric on labels, and let 7 >= 10? denote the @ nearest neighbors of item 0 according to some itemsimilarity function...

9Large Scale Acquisition of Paraphrases for Learning Surface Patterns2008  Rahul Bhagat,Deepak Ravichandrancan find the paraphrases for each pi by finding its nearest neighbors. We use cosine similarity, which is a commonly used...

10Highquality Training Data Selection using Latent Topics for Graphbased Semisupervised Learning2013  Akiko Eriguchi,Ichiro Kobayashiwij = sim(xi,xj)δ(j ∈ K(i)). K(i) is a set of i’s knearest neighbors, and δ(z) is 1 if z is true, otherwise 0. 3.2 Similarity...