Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning.

Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail. As more customers start purchasing consumable products (e.g., toothpastes, diapers, etc.) online, this phenomenon has also become prevalent in e-commerce. However, in January 2014, when we looked at popular e-commerce websites, we did not find any customer-facing features that recommended products to customers from their purchase history to promote repeat purchasing. Also, we found limited research about repeat purchase recommendations and none that deals with the large scale purchase data that e-commerce websites collect. In this paper, we present the approach we developed for modeling repeat purchase recommendations. This work has demonstrated over 7% increase in the product click through rate on the personalized recommendations page of the Amazon.com website and has resulted in the launch of several customer-facing features on the Amazon.com website, the Amazon mobile app, and other Amazon websites.

Paraphrases are sentences or phrases that convey the same meaning using different wording. Although the logical definition of paraphrases requires strict semantic equivalence, linguistics accepts a broader, approximate, equivalence—thereby allowing far more examples of “quasi-paraphrase.” But approximate equivalence is hard to define. Thus, the phenomenon of paraphrases, as understood in linguistics, is difficult to characterize. In this article, we list a set of 25 operations that generate quasi-paraphrases. We then empirically validate the scope and accuracy of this list by manually analyzing random samples of two publicly available paraphrase corpora. We provide the distribution of naturally occurring quasi-paraphrases in English text.

Paraphrases are textual expressions that convey the same meaning using different surface forms. Capturing the variability of language, they play an important role in many natural language applications includ ing question answering, machine translation, and multi-document summarization. In linguistics, paraphrases are characterized by approximate conceptual equivalence. Since no automated semantic interpretation systems available today can identify conceptual equivalence, paraphrases are difficult to acquire without human effort. In this paper, we present a method for automatically acquiring paraphrases using a monolingual corpus. We learn paraphrases at both the surface and lexico-syntactic levels and build two paraphrase resources each containing about 2 million phrases. We evaluate these paraphrases extrinsically by using them to learn patterns for Information Extraction (IE). We show that the lexico-syntactic paraphrases performs better than the surface-level paraphrases for IE. We further show that the patterns learned using the lexico-syntactic paraphrases attain comparable performance to the traditional IE approach of learning patterns from domain-specific corpora.

We present eArchivarius an interactive system for accessing collections of electronic mail. The system combines search, clustering visualization, and time-based visualization of email messages and people who send or received the messages.