A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.Comment: Accepted for publication at The 13th International Conference on Computational Semantics (IWCS 2019

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

Modelling Structures for Situated Discourse

This paper describes a corpus of situated multiparty chats developed for the STAC project (Strategic Conversation, ERC grant n. 269427). and annotated for discourse structure in the style of Segmented Discourse Representation Theory (SDRT; Asher & Lascarides,2003).  The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides discourse structures for multiparty dialogues situated within a virtual environment.  The corpus was annotated in two stages: we initially annotated the chat moves only, but later decided to annotate interactions between the chat moves and non-linguistic events from the virtual environment. This two-step procedure  has allowed us quantify various ways in which adding information from the nonlinguistic context affects dialogue structure.  In this paper, we  look at how annotations based only on linguistic information were preserved once the nonlinguistic context was factored in.  We explain that while the preservation of relation instances is relatively high when we move from one corpus to the other, there is little preservation of higher order structures that capture "the main point" of a dialogue and distinguish it from peripheral information

Natural language processing and advanced information management

Integrating diverse information sources and application software in a principled and general manner will require a very capable advanced information management (AIM) system. In particular, such a system will need a comprehensive addressing scheme to locate the material in its docuverse. It will also need a natural language processing (NLP) system of great sophistication. It seems that the NLP system must serve three functions. First, it provides an natural language interface (NLI) for the users. Second, it serves as the core component that understands and makes use of the real-world interpretations (RWIs) contained in the docuverse. Third, it enables the reasoning specialists (RSs) to arrive at conclusions that can be transformed into procedures that will satisfy the users' requests. The best candidate for an intelligent agent that can satisfactorily make use of RSs and transform documents (TDs) appears to be an object oriented data base (OODB). OODBs have, apparently, an inherent capacity to use the large numbers of RSs and TDs that will be required by an AIM system and an inherent capacity to use them in an effective way

Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation

We describe an implemented system for robust domain-independent syntactic parsing of English, using a unification-based grammar of part-of-speech and punctuation labels coupled with a probabilistic LR parser. We present evaluations of the system's performance along several different dimensions; these enable us to assess the contribution that each individual part is making to the success of the system as a whole, and thus prioritise the effort to be devoted to its further enhancement. Currently, the system is able to parse around 80% of sentences in a substantial corpus of general text containing a number of distinct genres. On a random sample of 250 such sentences the system has a mean crossing bracket rate of 0.71 and recall and precision of 83% and 84% respectively when evaluated against manually-disambiguated analyses.Comment: 10 pages, 1 Postscript figure. To Appear in Proceedings of the Conference on Empirical Methods in Natural Language Processing, University of Pennsylvania, May 199

Interleaving natural language parsing and generation through uniform processing

We present a new model of natural language processing in which natural language parsing and generation are strongly interleaved tasks. Interleaving of parsing and generation is important if we assume that natural language understanding and production are not only performed in isolation but also can work together to obtain subsentential interactions in text revision or dialog systems. The core of the model is a new uniform agenda-driven tabular algorithm, called UTA. Although uniformly defined, UTA is able to configure itself dynamically for either parsing or generation, because it is fully driven by the structure of the actual input - a string for parsing and a semantic expression for generation. Efficient interleaving of parsing and generation is obtained through item sharing between parsing and generation. This novel processing strategy facilitates exchanging items (i.e., partial results) computed in one direction automatically to the other direction as well. The advantage of UTA in combination with the item sharing method is that we are able to extend the use of memorization techniques even to the case of an interleaved approach. In order to demonstrate UTA\u27s utility for developing high-level performance methods, we present a new algorithm for incremental self-monitoring during natural language production