Text Understanding is a long term goal of Artificial Intelligence. Its mission is to create algorithms that will fully automatically understand human-composed text. In this dissertation we propose, implement and evaluate machine learning approaches and meaning representations to make progress towards Text Understanding. Meaning representations are used to encode the information contained in a sentence. Furthermore, they can be used in applications that require detailed understanding of the sentence.

For this assignment we will use your average F1 score to evaluate the correctness of the CKY parser, although in essence we ought to know from the output on the development set (devtest) whether the parser is implemented correctly. The following figure shows all the training trees. There are just enough productions in the training set for the test sentence to have an ambiguity due to PP-attachment. The following figure shows the PCFG learnt from these training trees. Now let's try to parse a single test sentence'cats scratch walls with claws' with the CKY parser and using the PCFG grammar learnt.

Ten years ago, two CoNLL shared tasks were a major milestone for parsing research in general and dependency parsing in particular. For the first time dependency treebanks in more than ten languages were available for learning parsers; many of them were used in follow-up work, evaluating parsers on multiple languages became a standard; and multiple state-of-the art, open-source parsers became available, facilitating production of dependency structures to be used in downstream applications. While the 2006 and 2007 tasks were extremely important in setting the scene for the following years, there were also limitations that complicated application of their results: 1. gold-standard tokenization and tags in the test data moved the tasks away from real-world scenarios, and 2. incompatible annotation schemes made cross-linguistic comparison impossible. CoNLL 2017 will pick up the threads of the pioneering tasks and address these two issues. The focus of the 2017 task is learning syntactic dependency parsers that can work in a real-world setting, starting from raw text, and that can work over many typologically different languages, even surprise languages for which there is little or no training data, by exploiting a common syntactic annotation standard.

Welcome to Essential Education, our daily look at education in California and beyond. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today.

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.

The classpath must include all of the CoreNLP dependencies. The memory requirements of the server are the same as that of CoreNLP, though it will grow as you load more models (e.g., memory increases if you load both the PCFG and Shift-Reduce constituency parser models). A safe minimum is 4gb; 8gb is recommended if you can spare it. If running the server under docker, the container's port 9000 has to be published to the host.