Fast Large-Scale Approximate Graph Construction for NLP. Amit Goyal, Hal Daum´e III, Raul Guerra. EMNLP 2012

• Claims:
• In FLAG, we first propose a novel distributed online-PMI algorithm
• We propose novel variants of PLEB to address the issue of reducing the pre-processing time for PLEB.
• Finally, we show the applicability of large-scale graphs built from FLAG on two applications: the Google-Sets problem  and learning concrete and abstract words.

Sketch Algorithms for Estimating Point Queries in NLP. Amit Goyal, Hal Daum´e III, Graham Cormode. EMNLP 2012

• Claims
• We propose novel variants of existing sketches by extending the idea of conservative update to them.
• We empirically compare and study the errors in approximate counts for several sketches.
• We use sketches to solve three important NLP problems: pseudo-words, semantic orientation (pos/neg), distributional similarity (using PMI and LLR).

Automatic Evaluation of Topic Coherence. David Newman, Jey Han Lau, Karl Grieser, Timothy Baldwin. NAACL-HLT 2010

• Claims
• we develop methods for evaluating the quality of a given topic, in terms of its coherence to a human (intrinsic qualitative evaluation).
• we ask humans to [judge] topics, propose models to predict
  topic coherence, demonstrate that our methods achieve nearly perfect agreement with humans

Multi-Prototype Vector-Space Models of Word Meaning. Joseph Reisinger, Raymond J. Mooney. NAACL-HLT 2010

• Claims
• We present a new resource-lean vector-space model that represents a word’s meaning by a set of distinct “sense specific” vectors.
• The model supports judging the similarity of both words in isolation and words in context.