Then a tree of concepts is chosen from the part of the concept lattice by iter-
atively going from the greatest concept (generated by no attributes or, equiva-
lently, by all objects) to minimal concepts. The selection is based on the number
$l_c$ of lower concepts of the currently considered concept c.

4.5 Online selection of passages for a query

At search time, to select the passage, we match the query with a chain of phrases for a path in a DecTSup. We try to find such DecTSup so that as many chain phrases path(i) match query phrases as possible.

$$
DecTSup : \sum_{i} score(q \land path(i)_{DecTSup}) \rightarrow max
$$

∧ is a syntactic generalization operator [5].

Once a single DecTSup is identified, we select the passages $p_s$ associated with matched nodes of DecTSup.

In some cases, the best match between the query and path phrases can be
distributed through multiple DecTSups. Then the condition of connectedness in
the lattice L of DecTSups must be maintained:

$$
\begin{align*}
DecTSups(j) : \sum_{i,j} & score(q \land path(i)DecTSup(j)) \rightarrow max \ \& \\
& \ \& node(DecTSups(j) \in DecTSups(k)) \in L.
\end{align*}
$$

To summarize the preference for the occurrence of an answer in a corpus
of documents, we prefer it to be in a single passage. If it is not possible, we
extend this passage towards other passages connected vis a DecTSup. If it is still
insufficient for answer identification, we further extend passages towards foreign
DecTSups linked in a lattice. Finally, in the worst case scenario, passages come
from unrelated documents associated with DecTSups anywhere in the lattice.

Linked lattice nodes correspond to passages containing the same entities plus
minus one or two, therefore, closely related. This is maintained by how the lattice
is defined on the set of all DecTSups.

5 Evaluation

We check the MRC model performance on the collection of medical instruction
collected from the WebMD website and syntactically generate the question that
could be asked regarding the retrieved passages. We select ten classes of dis-
eases to diversify the experiments, and track each processing step to identify the
performance bottleneck. It should be mentioned that the F1-score reported to
assess the model's performance is applied for each topic independently assessing
whether a retrieved answer is correct or not.

In the baseline, we rely on IR to identify passages in the fixed corpus of doc-
uments, so the main source of errors is an improperly selected passage or a lack
<table><caption>**Table 2.** Evaluation results for the IR in medical domain</caption><thead><tr><th>Search domain</th><th>Baseline (IR-based)</th><th>F1</th><th>F1 with synt. gen. between q and p</th><th>F1 with a set of DecTSups</th><th>F1 with a lattice of DecTSups</th></tr></thead><tbody><tr><td>Bloating</td><td>76.3</td><td>77.0</td><td>79.3</td><td>80.4</td></tr><tr><td>Cough</td><td>75.2</td><td>76.8</td><td>76.9</td><td>82.4</td></tr><tr><td>Diarrhea</td><td>77.0</td><td>75.9</td><td>78.0</td><td>79.3</td></tr><tr><td>Dizziness</td><td>77.9</td><td>77.1</td><td>79.8</td><td>81.9</td></tr><tr><td>Fatigue</td><td>72.6</td><td>75.4</td><td>80.4</td><td>82.7</td></tr><tr><td>Fever</td><td>71.9</td><td>74.5</td><td>78.0</td><td>79.3</td></tr><tr><td>Headache</td><td>74.6</td><td>74.8</td><td>81.5</td><td>82.7</td></tr><tr><td>Muscle Cramp</td><td>77.3</td><td>76.0</td><td>80.6</td><td>81.0</td></tr><tr><td>Nausea</td><td>72.1</td><td>76.4</td><td>78.4</td><td>80.7</td></tr><tr><td>Throat irritation</td><td>75.9</td><td>73.2</td><td>79.5</td><td>82.3</td></tr><tr><td>Average</td><td>75.1</td><td>75.7</td><td>79.2</td><td>81.3</td></tr></tbody></table>

of appropriate passage in the corpus. Our second baseline in the third column is syntactic generalization between query *q* and passage *p* which is better than keyword frequency maintained by IR but lags behind the approach developed in this paper. In the fourth column, we show F1 of DecTSup-enabled MRC environment. The fifth column shows the contribution of ideal, corrected DecTSups and the last, sixth column should the contribution of the lattice of DecTSups, where individual trees are organized to systematically identify additional passages which need to be involved in finding the exact answer.

The second baseline is only 0.6% better than the IR-based document identification, which makes the syntactic generalization insufficient for the robust identification of relevant passages to form the exact comprehensive answer. We observe that an unstructured MRC can have a boost of 4% F1-score by finding the best document by DecTSup environment. Proceeding from IR passage selection to the one based on independent DecTSups turns out to be an ultimate win for the MRC environment. A further upgrade from a set of DecTSups to a lattice gives further 2% boost in search performance.

## 6 Conclusion

In this work, we explored a way to build decision trees from text relying on discourse analysis and use this environment to boost the MRC model's performance. We use DecTSup environment for retrieving the relevant documents and passages, where the answer can be found. We compare the DecTSup-based retrieval procedures with several baselines including syntactic generalization for matching questions and passages and keywords matching on the set of the medical instructions texts. We show that utilizing the DecTSup as the description of texts and combining them into the concept lattice improves the performance of MRC by up to 6% on average. In future studies, we will consider building a
concept lattice from a textual description of data [5], instead of a decision tree
for authors' instructions on how to do things and make decisions in the course
of it.

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