Shallow parsing

About: Shallow parsing is a research topic. Over the lifetime, 397 publications have been published within this topic receiving 10211 citations.

TREPIL: Developing Methods and Tools for Multilevel Treebank Construction

Abstract: Current trends in language technology require treebanks that do not stop at the level of constituent structure, but include deeper and richer levels of analysis, including appropriate meaning structures. Capturing sufficient detail at different levels of linguistic description is too complex a task to be practically achievable by manual annotation or shallow parsing; rather it requires sophisticated tools that help secure the consistency of parallel but different structures. In conventional treebanks, grammatical functions and semantic roles are often simply attached to the syntactic constituent structure. The Penn Proposition Bank [12, 20] is basically constructed by labeling verbs as predicates and assigning appropriate semantic (thematic) roles to syntactic constituents that are in grammatical relations to the verbs. Though useful in its own right, this approach is nevertheless limited to verbs and is constrained by implicit isomorphism between the syntactic and semantic structures. In contrast, we are constructing a multilevel treebanking tool that incorporates a deep parser and grammar for Norwegian. Inspired by the LinGO Redwoods approach [19], we are tightly linking our treebank to grammar development so as to achieve a sound embedding in grammatical theory and yield more useful results for applications.

Natural Language Interface Using Shallow Parsing.

Abstract: This paper deals with a natural language interface, which accepts natural language questions as inputs and generates textual responses. In natural language processing, key-word matching based paradigm generate answers, however these answers frequently affected by certain language dependant phenomena such as semantic symmetry and ambiguous modification. Available techniques, described in the literature, deal with these problems using in depth parsing. In this paper, we will present rules to tackle linguistic phenomena using shallow parsing and discuss advantages of a novel Natural Language Interface comprising of shallow parsing based algorithms in conjunction with some intelligent techniques to train the system. Experimental results show that this approach can analyze a wide range of questions with high accuracy and produce reasonable textual responses.

Multilingual Ontology Acquisition from Multiple MRDs

Abstract: In this paper, we outline the development of a system that automatically constructs ontologies by extracting knowledge from dictionary definition sentences using Robust Minimal Recursion Semantics (RMRS). Combining deep and shallow parsing resource through the common formalism of RMRS allows us to extract ontological relations in greater quantity and quality than possible with any of the methods independently. Using this method, we construct ontologies from two different Japanese lexicons and one English lexicon. We then link them to existing, handcrafted ontologies, aligning them at the word-sense level. This alignment provides a representative evaluation of the quality of the relations being extracted. We present the results of this ontology construction and discuss how our system was designed to handle multiple lexicons and languages.

Word Level Language Identification in English Telugu Code Mixed Data

Abstract: In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world, most of the people know more than one language. CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of technology, health, and law where conveying the upcoming developments are difficult in one's native language. In applications like dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM and Code Switching pose serious challenges. To do any further advancement in code-mixed data, the necessary step is Language Identification. In this paper, we present a study of various models - Nave Bayes Classifier, Random Forest Classifier, Conditional Random Field (CRF), and Hidden Markov Model (HMM) for Language Identification in English - Telugu Code Mixed Data. Considering the paucity of resources in code mixed languages, we proposed the CRF model and HMM model for word level language identification. Our best performing system is CRF-based with an f1-score of 0.91.

An environment for document engineering of clinical guidelines.

Abstract: In this paper, we present the G-DEE system, a document engineering environment aimed at clinical guidelines. This system represents an extension of current visual interfaces for guidelines encoding, in that it supports automatic text processing functions which identify linguistic markers of document structure, such as recommendations, thereby decreasing the complexity of operations required by the user. Such markers are identified by shallow parsing of free text and are automatically marked up as an early step of document structuring. From this first representation, it is possible to identify elements of guidelines contents, such as decision variables, and produce elements of GEM encoding, using rules defined as XSL style sheets. We tested our automatic structuring system on a set of sentences extracted from French clinical guidelines. As a result, 97% of the occurrences of deontic operators and their scopes were correctly marked up. G-DEE can be used for various purposes, from research into guidelines structure to assisting the encoding of guidelines into a GEM format or into decision rules.