MQL versus TF-Query¶

See TF versus MQL for an introduction.

Loading¶

We load the Text-Fabric program and the BHSA data.

Example 9¶

Oliver Glanz: DHQ article: discourse pattern deviation

[[clause domain = "N"
  [phrase function = Pred
    [word
      [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
  ]
    ..
    [phrase FOCUS function = Subj
      [word AS samesubject]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word AS samecomplement]
    ]
  ]
  [clause domain = "N"]* {0-1}
  [clause domain = "Q"]* {1-50}
  [clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Subj
      [word lex = samesubject.lex]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word lex = samecomplement.lex]
    ]
  ]]
OR
  [[clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word AS samecomplement2]
    ]
    ..
    [phrase FOCUS function = Subj
      [word AS samesubject2]
    ]
  ]
  [clause domain = "N"]* {0-1}
  [clause domain = "Q"]* {1-50}
  [clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word lex = samecomplement2.lex]
    ]
    ..
    [phrase FOCUS function = Subj
      [word lex = samesubject2.lex]
    ]
  ]]
  OR
  [[clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Subj
      [word AS samesubject3]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word AS samecomplement3]
    ]
  ]
  [clause domain = "N"]* {0-1}
  [clause domain = "Q"]* {1-50}
  [clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word lex = samecomplement3.lex]
    ]
    ..
    [phrase FOCUS function = Subj
      [word lex = samesubject3.lex]
    ]
  ]]
  OR
  [[clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word AS samecomplement4]
    ]
    ..
    [phrase FOCUS function = Subj
      [word AS samesubject4]
    ]
  ]
  [clause domain = "N"]* {0-1}
  [clause domain = "Q"]* {1-50}
  [clause domain = "N"
    [phrase function = Pred
      [word
        [word lex = "DBR["]
        OR
        [word lex = ">MR["]
        OR
        [word lex = "QR>["]
      ]
    ]
    ..
    [phrase FOCUS function = Subj
      [word lex = samesubject4.lex]
    ]
    ..
    [phrase FOCUS function = Cmpl
      [word lex = samecomplement4.lex]
    ]
  ]]

This is a complex query. Let's make it simpler first.

We see some recurring objects:

speakPhrase

[phrase function = Pred
  [word
    [word lex = "DBR["]
      OR
      [word lex = ">MR["]
      OR
      [word lex = "QR>["]
    ]
]

subjPhrase

[phrase FOCUS function = Subj
  [word AS samesubject]
]

cmplPhrase

[phrase FOCUS function = Cmpl
  [word AS samecomplement]
]

clauses

[clause domain = "N"]* {0-1}
[clause domain = "Q"]* {1-50}

The structure of the whole query is then, in pseudo TF-query terms:

clause domain=N
  speakPhrase
  < s:subjPhrase
  < c:cmplPhrase
clauses
clause domain=N
  speakPhrase
  < subjPhrase (.lex. s)
  < cmplPhrase (.lex. c)

OR

clause domain=N
  speakPhrase
  < c:cmplPhrase
  < s:subjPhrase
clauses
clause domain=N
  speakPhrase
  < cmplPhrase (.lex. c)
  < subjPhrase (.lex. s)

OR

clause domain=N
  speakPhrase
  < s:subjPhrase
  < c:cmplPhrase
clauses
clause domain=N
  speakPhrase
  < cmplPhrase (.lex. c)
  < subjPhrase (.lex. s)

OR

clause domain=N
  speakPhrase
  < c:cmplPhrase
  < s:subjPhrase
clauses
clause domain=N
  speakPhrase
  < subjPhrase (.lex. s)
  < cmplPhrase (.lex. c)

The OR is only used to enumerate the four different orders between subjPhrase and cmplPhrase. So we can simplify greatly!

clause domain=N
  sp1:speakPhrase
  < s1:subjPhrase
  c1:cmplPhrase
< clauses
< clause domain=N
  sp2:speakPhrase
  < s2:subjPhrase (.lex. s1)
  c2:cmplPhrase (.lex. c1)

sp1 < c1
sp2 < c2
s1 .lex. s2
c1 .lex. c2

There is a problem with translating the clauses bit:

[clause domain = "N"]* {0-1}
[clause domain = "Q"]* {1-50}

The operator * {n-m} means: repeat the previous block n to m times. In TF-Query there is no such operator.

We will mimic this query by means of a mixture of TF-Query and hand-coding.

We examine the results of searching for

clause domain=N
  sp: speakPhrase
  < subjPhrase
  c:cmplPhrase

sp < c

By means of hand coding we walk through the results of this query:

1. suppose we are at a query result
2. walk through the following clauses as long as they match clauses
3. see if the next clause is a result of the query
4. check whether this clause and the one we started at in 1. agree lexically in their subjPhrase and cmplPhrase
5. for each such pair of results we add the combination to the result set

We take care to deliver the results in the same way as a TF-query would do.

N.B. What does agree mean in 4? According to the MQL query:

[phrase FOCUS function = Subj
      [word AS samesubject]
]
..
[phrase FOCUS function = Cmpl
  [word AS samecomplement]
]

That means that two phrases agree if they both have a word which are an occurrence of the same lexeme. In short: they share a lexeme.

That is a fairly relaxed condition: if both phrases have the article, or the same preposition, the condition is met. BUt that is what the query says, and we stick to that.

This is our starting point.

We are going to weave these results together.

The following function does that. It has to find up to 50 intervening clauses with domain=Q between two clauses with a speech verb.

Let's parametrize this number 50, so that we can play with it later on.

What if we allowed only strings of 49 Q-clauses? Would that matter?

It would be nice if we could see the number of Q-clauses in each result. Well, we have sneaked that in already! It is added at the end of each result tuple.

Here are the minimum and the maximum that we encountered:

So we expect that it does matter if we go from 50 to 49. Before we test that, let us show all Q-lengths:

Let's double-check: if we allow only strings of Q-clauses up to length 49, the result in Leviticus 21:1 should be gone.

And so it is!

Conclusion

Instead of running a query and obtaining a list of results, we did a bit of programming and we can get much more than just the results.

That is the power of programming. But programming is difficult, and mistakes will be made.

TF-Query helps you to find a sweet spot between crafting queries and writing code.