Most words in natural languages are polysemous in nature that is they have multiple possible meanings or senses. The sense in which the word is used determines the translation of the word. We show that incorporating a sense-based translation model into statistical machine translation model consistently improves translation quality across all different test sets of five different language-pairs, according to all eight most commonly used evaluation metrics. This paper is an investigation on how to initiate research in word sense disambiguation and statistical machine translation for under-resourced languages by applying Word Sense Induction.
WordNets are useful resources for natural language processing. Various WordNets for different languages have been developed by different groups. Recently, World WordNet Database Structure (WWDS) was proposed by Redkar et. al (2015) as a common platform to store these different WordNets. However, it is underutilized due to lack of programming interface. In this paper, we present WWDS APIs, which are designed to address this shortcoming. These WWDS APIs, in conjunction with WWDS, act as a wrapper that enables developers to utilize WordNets without worrying about the underlying storage structure. The APIs are developed in PHP, Java, and Python, as they are the preferred programming languages of most developers and researchers working in language technologies. These APIs can help in various applications like machine translation, word sense disambiguation, multilingual information retrieval, etc.
Business users across enterprises today rely on reports and dashboards created by IT organizations to understand the dynamics of their business better and get insights into the data. In many cases, these users are underserved and do not possess the technical skillset to query the data source to get the information they need. There is a need for users to access information in the most natural way possible. AI-based Business Analysts are going to change the future of business analytics and business intelligence by providing a natural language interface between the user and data. This natural language interface can understand ambiguous questions from users, the intent and convert the same into a database query. One of the important elements of an AI-based business analyst is to interpret a natural language question. It also requires identification of key business entities within the question and relationship between them to generate insights. The Artificial Named Entity Classifier (ANEC) helps us take a huge step forward in that direction by not only identifying but also classifying entities with the help of the sequence recognising prowess of BiLSTMs.
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