Recent advances in neural word embedding provide significant benefit to
various information retrieval tasks. However as shown by recent studies,
adapting the embedding models for the needs of IR tasks can bring considerable
further improvements. The embedding models in general define the term
relatedness by exploiting the terms' co-occurrences in short-window contexts.
An alternative (and well-studied) approach in IR for related terms to a query
is using local information i.e. a set of top-retrieved documents. In view of
these two methods of term relatedness, in this work, we report our study on
incorporating the local information of the query in the word embeddings. One
main challenge in this direction is that the dense vectors of word embeddings
and their estimation of term-to-term relatedness remain difficult to interpret
and hard to analyze. As an alternative, explicit word representations propose
vectors whose dimensions are easily interpretable, and recent methods show
competitive performance to the dense vectors. We introduce a neural-based
explicit representation, rooted in the conceptual ideas of the word2vec
Skip-Gram model. The method provides interpretable explicit vectors while
keeping the effectiveness of the Skip-Gram model. The evaluation of various
explicit representations on word association collections shows that the newly
proposed method out- performs the state-of-the-art explicit representations
when tasked with ranking highly similar terms. Based on the introduced ex-
plicit representation, we discuss our approaches on integrating local documents
in globally-trained embedding models and discuss the preliminary results.

本文针对词嵌入模型在信息检索任务中存在的问题及其局限性，提出了一种基于 word2vec Skip-Gram 模型的神经网络模型，通过显式向量表示的方式，增加了模型的可解释性和准确性，实现了查询词汇的局部信息融合，提高了指定任务状态下的表现表现。