Technical English Course (part 4)

Previously… on Technical English course: part 1 – formal writing style and utilizing a concordancer, part 2 – all about articles, and lastly, part 3 – all about choices and pronoun one.

#10 Defining vs non-defining (extra information) relative clauses

One simple rule is that non-defining relative clauses, a.k.a extra information, are always distinguished from the main sentence with commas. It is easier to learn by examples, isn’t it? So here we go…

Everest which is the highest mountain in the world was not climbed until 1953.
Everest, which is the highest mountain in the world, was not climbed until 1953.
You don’t need to define which Everest. It’s just extra information about Everest.

The river that runs though Paris is called the Seine.
The river that runs through Paris, is called the Seine.
Don’t separate the subject ‘the river that runs through Paris’ and its verb with a comma.

The Pyrenees, which divide Spain from France, are often covered with snow.
The Pyrenees, that divide Spain from France, are often covered with snow.
No ‘that’ in a non-defining clause (extra information).

Could you please let us know the dates, which would suit you best?
Could you please let us know the dates which would suit you best?
Defining which dates are the subject of discussion.

The girls who worked hard were given a bonus.
The girls, who worked hard, were given a bonus.
Both possible. In the first ONLY the ones who worked hard got a bonus. In the second, all worked
hard, so all got a bonus.

And one more important rule: never use that in a non-defining relative clause (extra information)!

#11 All about paraphrasing

1. Reduced relative clauses

We conclude the paper with a brief section that summarizes our conclusions.
We conclude the paper with a brief section summarizing our conclusions.

The nurse who was hired by the doctor was very young.
The nurse hired by the doctor was very young.

The only theory which was possible did not account for all the data.
The only theory possible did not account for all the data.

However, not everything can be reduced, as shown in the following sentences…

This algorithm generates a schema which makes the translation algorithm easy to understand.
The computer is a system which is simple enough for a child to use.
Stars which are red are older than stars which are blue.

In the first sentence, the whole point of the sentence is telling us about the schema, reducing it will change the meaning. The same as in the second sentence, because ‘which is simple enough’ is the main point. In the third sentence, it’s not possible to reduce it into ‘Stars red’ as you can with ‘theory possible’.

2. Subjunctive form instead of modal verb

Formal writing style favors the usage of subjunctive form instead of modal verb (must, shall and should). Consider this sentence: “A radiation badge must be worn in the reactor control room.” We could change it into these following forms:

The company demands that a radiation badge be worn in the reactor control room. (verb)
It is required that a radiation badge be worn in the reactor control room.                (passive)
It is essential that a radiation badge be worn in the reactor control room.                (adjective)
There is a requirement that a radiation badge be worn in the reactor control room.  (noun)

modal verb adjective noun
NECESSITY
must
shall
ask
command
demand
direct
insist
require
compulsory
crucial
essential
imperative
necessary
obligatory
vital
demand
direction
order
requirement
RECOMMENDATION
should
propose
recommend
request
suggest
urge
desire
advisable
desirable
fitting
preferable
urgent
desire
proposal
recommendation
suggestion

 3. Participle clauses

Noun + present participle is usually used to combine two sentences or clauses, to create more compact (and sophisticated :p) writing style. Let’s see the examples…

We break the probability estimation into two parts.  The first is the probability of…
We break the probability estimation into two parts, the first being the probability of…

Users can select from these templates to apply during plan development. The system provides various forms of automated assistance.
Users can select from these templates to apply during plan development, with the system providing various forms of automated assistance.

The input covers all five modes of the emulated structure, and the highest has a frequency of 7.7 Hz.
The input covers all five modes of the emulated structure, the highest having a frequency of 7.7 Hz.

#12 Mistake detection

Gathering all the knowledge previously explained, our last task is to spot mistakes occurring in a text. This was actually the first problem in the course exam, the second being a writing task — writing an introduction section for a given paper.

“Semantic-rich NLP has been tackled over the years from many systems that wed, with various emphases, work on semantic microtheories, representation terms, interlingua, ontologies and approaches to central problem of ambiguity resolution. While one can distinguish communities basing on a primary area of interest, real systems don’t know any such theoretical boundaries. In this paper we discuss a semantic-rich text processing environment that has become known as OntoSem, which incorporates an ontology and related resources that allow to support multi-lingual text processing (the goal of the approach has been interlingual machine translation originally). This OntoSem ontology is linked with lexicons for each of language processed, whose semantic structures represent another level of interlingual semantic representation.  Among its other goals, OntoSem aims to solve the same problems of ambiguity resolution as controlled vocabularies do, but reaching over a language’s entire vocabulary. Unambiguous, language-neutral semantic representation is expressed in what we call Text-Meaning Representations (TMRs), that are automatically generated representations written in an ontology-grounded metalanguage.”

— An Implemented, Integrative Approach to Ontology-Based NLP and Interlingua, Marjorie McShane, Sergei Nirenburg and Stephen Beale. Working Paper 06-05.

There are 10 mistakes, each belongs to these following types of mistake:

  • Incorrect pronoun in extra information clause
  • Incorrect tense
  • Inappropriate expression of possession
  • Incorrect preposition
  • Extra preposition
  • Incorrect grammar with a verb
  • Incorrect participle form
  • Insufficiently formal position of adverb
  • Missing article (where it is obligatory)
  • Insufficiently formal negative form

Curious about the answer? 😉 Here is the key… [spoiler]

Semantic-rich NLP has been tackled over the years from (incorrect preposition) many systems that wed, with various emphases, work on semantic microtheories, representation terms, interlingua, ontologies and approaches to central problem (missing article) of ambiguity resolution. While one can distinguish communities basing (incorrect participle form) on a primary area of interest, real systems don’t know any (insufficiently formal negative form) such theoretical boundaries. In this paper we discuss a semantic-rich text processing environment that has become known as OntoSem, which incorporates an ontology and related resources that allow to (incorrect grammar with a verb) support multi-lingual text processing (the goal of the approach has been (incorrect tense) interlingual machine translation originally (insufficiently formal position of adverb)). This OntoSem ontology is linked with lexicons for each of language (extra preposition) processed, whose semantic structures represent another level of interlingual semantic representation.  Among its other goals, OntoSem aims to solve the same problems of ambiguity resolution as controlled vocabularies do, but reaching over a language’s (inappropriate expression of possession) entire vocabulary. Unambiguous, language-neutral semantic representation is expressed in what we call Text-Meaning Representations (TMRs), that (incorrect pronoun in extra information clause) are automatically generated representations written in an ontology-grounded metalanguage.

[/spoiler]

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