Natural language semantics using probabilistic logic

With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single semantic representation at this time fulfills all requirements needed for a satisfactory representation. Logic-based representations like first-order logic capture many of the linguistic phenomena using logical constructs, and they come with standardized inference mechanisms, but standard first-order logic fails to capture the “graded” aspect of meaning in languages. Other approaches for semantics, like distributional models, focus on capturing “graded” semantic similarity of words and phrases but do not capture sentence structure in the same detail as logic-based approaches. However, both aspects of semantics, structure and gradedness, are important for an accurate language semantics representation. In this work, we propose a natural language semantics representation that uses probabilistic logic (PL) to integrate logical with weighted uncertain knowledge. It combines the expressivity and the automated inference of logic with the ability to reason with uncertainty. To demonstrate the effectiveness of our semantic representation, we implement and evaluate it on three tasks, recognizing textual entailment (RTE), semantic textual similarity (STS) and open-domain question answering (QA). These tasks can utilize the strengths of our representation and the integration of logical representation and uncertain knowledge. Our semantic representation 1 has three components, Logical Form, Knowledge Base and Inference, all of which present interesting challenges and we make new contributions in each of them. The first component is the Logical Form, which is the primary meaning representation. We address two points, how to translate input sentences to logical form, and how to adapt the resulting logical form to PL. First, we use Boxer, a CCG-based semantic analysis tool to translate sentences to logical form. We also explore translating dependency trees to logical form. Then, we adapt the logical forms to ensure that universal quantifiers and negations work as expected. The second component is the Knowledge Base which contains “uncertain” background knowledge required for a given problem. We collect the “relevant” lexical information from different linguistic resources, encode them as weighted logical rules, and add them to the knowledge base. We add rules from existing databases, in particular WordNet and the Paraphrase Database (PPDB). Since these are incomplete, we generate additional on-the-fly rules that could be useful. We use alignment techniques to propose rules that are relevant to a particular problem, and explore two alignment methods, one based on Robinson’s resolution and the other based on graph matching. We automatically annotate the proposed rules and use them to learn weights for unseen rules. The third component is Inference. This component is implemented for each task separately. We use the logical form and the knowledge base constructed in the previous two steps to formulate the task as a PL inference problem then develop a PL inference algorithm that is optimized for this particular task. We explore the use of two PL frameworks, Markov Logic Networks (MLNs) and Probabilistic Soft Logic (PSL). We discuss which framework works best for a particular task, and present new inference algorithms for each framework.Computer Science

Semantic Parsing using Distributional Semantics and Probabilistic Logic

We propose a new approach to semantic parsing that is not constrained by a fixed formal ontology and purely logical infer-ence. Instead, we use distributional se-mantics to generate only the relevant part of an on-the-fly ontology. Sentences and the on-the-fly ontology are represented in probabilistic logic. For inference, we use probabilistic logic frameworks like Markov Logic Networks (MLN) and Prob-abilistic Soft Logic (PSL). This seman-tic parsing approach is evaluated on two tasks, Textual Entitlement (RTE) and Tex-tual Similarity (STS), both accomplished using inference in probabilistic logic. Ex-periments show the potential of the ap-proach.

Channel assignment with closeness multipath routing in cognitive networks

Routing in cognitive networks is a challenging problem due to the primary users’ (PU) activities and mobility. Multipath routing is a general solution to improve reliability of connections. Routes closeness metric was proposed for multipath routing in cognitive networks; however, the proposed technique supports only one channel [4]. This work proposes a multichannel assignment technique for multipath routing using routes closeness as the routing metric. It relies on the nodes of the different paths to early detect the existence of PUs and notify nodes on other routes to avoid using the PU’s channel that is going to be interrupted. In case the field has PUs occupying all channels, channels assigned to nodes based on how far the nodes are from the PU. Simulation results show the effectiveness of the channel assignment technique in increasing end-to-end throughput and decreasing delay