Armaan Bhatia
Corina Girju
May 6, 2016
LING 406 Report
Co-reference Resolution Detector
Introduction
Co-reference Resolution is the task of finding and identifying all expressions and references to the same entity in a discourse setting.
Co-referencing occurs when two or more expressions in a text refer to the same subject/object in the discourse context. It is important for us as linguists to be able to differentiate these references, and be able to successfully identify the varying pronouns/expressions pointing to the original referent in order to better parse and understand the text being analyzed. It is an important tool which helps us to better derive the correct interpretation of the text, or even with estimating of the relative importance of these mentioned subjects.
The problem at hand is backward pronoun anaphora resolution. It is important in terms of machine learning systems which may be used to successfully tag sentences, and find the various references throughout the text.
Previous Work & Examples
Using Co-Reference Resolution in Outbreak reporting and detection:
In 2010, an experimental analysis in the same field was used by the University of Utah, trained on publicly available documents (with data unrelated to health care) and then tested on outbreak reports. It was able to successfully train on an unrelated data-set and then resolve the appropriate references in public-health related documents.
They utilized and trained the CR detecter on the MUC corpora
The text book spoke of many different ways to approach citations. It is important when working amongst a mixed variety of intelligences to be a critical thinker. The ability to analyze and ask questions to develop a solution is extremely important, especially when the lives of the pilots and crews are at stake.
Referencing something that gives the reader an understanding that is familiar to the author shows that you understand the topic.
Referencing should be used whenever quotations of an original text is used or you refer to quotations and paraphrase any content that has been written by someone else. References could
He also identified the lexical level of language analysis the aim of which was to study the meaning of words either in isolation or in syntactic constructions. This marks his own contribution to semantics.
Donnellan, in “Reference and Definite Description,” outlines two uses of definite descriptions. He argues that definite descriptions can either have an attributive or a referential use. While theories outlined by Strawson and Russell account for a single use, Donnellan advocates for both. To explain both the attributive and referential uses, I will review two examples.
A word used to link the subject f a sentence with a predicate that associates the subject with the predicate.
CCSS.ELA-Literarcy.RI.1.3- describe the connection between two individuals, events, ideas, or pieces of information in a text
Refer to details and examples in a text when explaining what the text says explicitly and when drawing inferences from the text. (Scaffolding opportunity)
Also, ‘big data’ analytics and aggregated patient data may be able to alert providers to larger health trends such as potential outbreaks and which flu strains are prominent during each flu season.
What is an annotated bibliography? When and why is it used? Also, what are in-text citations and why do we use them? These are a few questions that we will be addressing in this paper, in addition to many other questions about these topics. Let us start by defining an annotated bibliography. As mentioned by Bullock et. al, “an annotated bibliography describes and gives publication information for, and sometimes evaluate each work on a list of sources.” (p.66) Additionally, an in-text citation is defined as “a reference made within the body of text of an academic essay” (“What is an in-text citation?”, para. 1) We will discover their purpose, why they are important, how to construct them, and when to use them.
This book is divided into 2 parts, the functions of language and language and thought. The deepest debt of the author in this book is to the general semantics. It is designed to educate the reader using concepts that are first explained in straightforward terms
The practice of personalized medicine that aims to individualize the diagnosis of a disease and therapy according to the individual patient’s characteristics, such as clinical comorbidities and risk factors, can base on decisions of evidences and guidelines derived from population-based studies and clinical trials. Big data can help study disease patterns across geographies, based on multiple factors that contribute to the incidence of a specific disease. Researchers can benefit from information about trends or changing disease patterns over time and more significantly, regarding location shifts in disease patterns. Pattern analysis can be further extended to various types of epidemic spreads in plants and animals. Epidemic early warning systems
Not at all like machine learning domain, routines that was in the factual classification incorporate manual configuration of the scientific recipes used to compute sentence scores. For instance, Sarkar et al. joined a few area particular components, for example, term recurrence, title and position and utilized a numerical equation to deliver extractive synopses in the therapeutic space. Common Language handling systems incorporates computational techniques connected to comprehend human dialects in a comparative way as it is prepared in talked and composed medium. This incorporates everything from straightforward applications like word tallying to powerful
Furthermore, it is apparent that English syntax, or the study of sentence structures is synonymous with much of the study of English grammar; Syntax in a reductive explanation is the study of sentence structure (Finegan, 2015, p. 178). In chapter 1 of Curzan (2003), theoretically, the evolution of English has eliminated its use of grammatical gender and this has
The team from Harbin Institute of Technology Shenzhen Graduate School (Chen et al., this issue), ranked 2nd, divided the risk factors into three categories: phrase-based, logic-based, and discourse-based. Phrase-based risk factors are those that are identified simply by finding relevant phrases in the text, such as “hyperlipidemia” or the name of a particular medication. Logic-based risk factors are those that require a form of analysis after identifying the relevant phrase, such as finding a blood pressure measurement and comparing the numbers to see if they are high enough to count as a risk factor. Finally, discourse-based risk factors are ones that require parsing a sentence, such as identifying smoking status or family history. After pre-processing