Models for Improved Tractability and Accuracy in Dependency Parsing

Automatic syntactic analysis of natural language is one of the fundamental problems in natural language processing. Dependency parses (directed trees in which edges represent the syntactic relationships between the words in a sentence) have been found to be particularly useful for machine translation, question answering, and other practical applications. For English dependency parsing, we show that models and features compatible with how conjunctions are represented in treebanks yield a parser with state-of-the-art overall accuracy and substantial improvements in the accuracy of conjunctions. For languages other than English, dependency parsing has often been formulated as either searching over trees without any crossing dependencies (projective trees) or searching over all directed spanning trees. The former sacrifices the ability to produce many natural language structures; the latter is NP-hard in the presence of features with scopes over siblings or grandparents in the tree. This thesis explores alternative ways to simultaneously produce crossing dependencies in the output and use models that parametrize over multiple edges. Gap inheritance is introduced in this thesis and quantifies the nesting of subtrees over intervals. The thesis provides O(n6) and O(n5) edge-factored parsing algorithms for two new classes of trees based on this property, and extends the latter to include grandparent factors. This thesis then defines 1-Endpoint-Crossing trees, in which for any edge that is crossed, all other edges that cross that edge share an endpoint. This property covers 95.8% or more of dependency parses across a variety of languages. A crossing-sensitive factorization introduced in this thesis generalizes a commonly used third-order factorization (capable of scoring triples of edges simultaneously). This thesis provides exact dynamic programming algorithms that find the optimal 1-Endpoint-Crossing tree under either an edge-factored model or this crossing-sensitive third-order model in O(n4) time, orders of magnitude faster than other mildly non-projective parsing algorithms and identical to the parsing time for projective trees under the third-order model. The implemented parser is significantly more accurate than the third-order projective parser under many experimental settings and significantly less accurate on none

The Origin and Development of Washington\u27s Independent Exclusionary Rule: Constitutional Right and Constitutionally Compelled Remedy

Underlying any court\u27s analysis of the exclusionary rule are certain basic theoretical elements that determine whether a court takes a unitary or a bifurcated approach to exclusion. To determine what theoretical elements underlie the Washington rule, the court must familiarize itself with the state rule\u27s long history of independent application, which has never been fully explored. The court must also recognize the historical relationship between the state exclusionary rule and certain provisions of the Declaration of Rights. 6 Analysis of the Washington exclusionary rule\u27s development reveals that, at minimum, exclusion is constitutionally compelled as the most effective remedy available to vindicate the defendant\u27s right to privacy. Moreover, failing to exclude violates the framers\u27 intention to incorporate the exclusionary rule in the state privilege against compelled self-incrimination contained in article 1, section 9. Finally, a failure to exclude eviscerates the defendant\u27s article 1, section 7 rights in violation of the state due process guarantee contained in article 1, section 3

Never Going Back: Lessons To Carry Forward In Online Instruction

Research has long demonstrated that students thrive best in an online learning community when some basic tenants are followed. These tenants include establishing a peer community, module supports, studying while balancing life commitments, confidence, and the approach to learning (Farrell & Brunton, 2020; Kahn, Egbue, Palkie, & Madden, 2017; Dixson, 2010). Cultivating active engagement in online communities is a purposeful and deliberate practice that requires educators to bring together an assortment of innovative instructional techniques to foster the establishment of Communities of Practice (COP). Wenger, Trayner, and de Laat (2011) define a CoP as a “learning partnership among people who find it useful to learn from and with each other about a particular domain” (p.9). At the start of the COVID-19 pandemic, the unexpected shift to online learning in schools at all levels caused schools of education to engage in “stop-gap” measures as they worked to move quality face-to-face instruction to online learning platforms so to allow students to continue their educational pathways. By contrast, the graduate programs in Curriculum & Instruction and Educational Administration at a small Midwestern University have been fully online for nearly two decades. While course delivery has naturally evolved during that time, past experiences allowed faculty to maneuver the pandemic and online learning seamlessly. This paper will explore what works well and should be carried forward in online teaching and learning

Natural Language Processing with Small Feed-Forward Networks

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.Comment: EMNLP 2017 short pape