At the morphological layer, the sequence of tokens of the w-layer is divided into sentences. Annotation of a sentence consists of attaching several attributes to the tokens of the w-layer, the most important of which are morphological lemma and tag.

Attribute lemma carries the lemma of the token. It represents its basic or normalized form, and it matches the unique key of the corresponding entry in the morphological dictionary. The tag attribute contains 15-character long morphological tag that expresses the part of speech of the token and its various morphological categories. The attribute id contains a PDT 2.0-unique identifier of the m-layer unit which is then used for back reference from the analytical layer (for the overall linking scheme, see Figure 2.1, "Linking the layers"), and the reference-type w.rf attribute refers back to the w-layer. Several other attributes handle possible (but rare) corrections and/or normalizations relative to the w-layer; the most important of them is the form attribute which contains the correct form of the text token (which might differ from the text token in case of spelling errors, incorrectly split or joined words, unsuitable representation of decimal points in numbers or of other technical problems with the original text).

See a sample sentence in Table 2.2, "Morphological analysis of the example sentence"

The morphological layer of PDT has been annotated by a group of seven annotators. The group proceeded in two separate phases. During the first phase-after each text had been processed by the automatic morphological analyzer-two annotators independently chose the lemma and the morphological tag from the list suggested by the morphological analyzer. In the second (adjudication) phase, an annotator-arbiter resolved the differences between them.

After the separate checking of the morphological and syntactical-analytical layers, a mutual revision was done. The comparison concentrated on the aspects of analytical functions vs. morphological tags, preposition vs. the case of depending node, and finally, the "case, gender, number" agreement between a dependent and its governing node.

A sentence at the analytical layer is represented as a rooted ordered tree with labeled nodes and edges. One token of the morphological layer (see Section 2.1, "Morphological layer") is represented by exactly one node of the tree and the dependency relation between two nodes is captured by an edge between the two nodes. The actual type of the relation is given as a function label of the edge. Most of the edges represent dependency relations, while others mirror various linguistic or technical phenomena, e.g. coordination, apposition, punctuation, etc. Linear ordering of the nodes, which corresponds to the original sentence word order, is also recorded, allowing "correct" graphical rendering of the tree.

A set of six attributes is attached to every node (except for the technical root of the tree that has less attributes). The attribute id contains a PDT 2.0-unique identifier of the node which is referred back (linked) from the tectogrammatical layer (see Figure 2.1, "Linking the layers"). The linear-order attribute ord contains the corresponding token position in the original sentence. For technical simplicity, the analytical function attribute afun belonging to an edge is moved to the dependent end of the edge and appears as a node attribute. The attributes is_member and is_parenthesis_root mark proper coordination, apposition and parenthesis interpretation. Finally, the attribute m.rf links the node to the corresponding morphological one.

See a sample tree in Figure 2.3, "The analytical tree of the example sentence"

All the analytical data have been annotated manually by a team of six annotators. At the beginning, the annotators had to build the whole tree and assign all the analytical functions, while in the later stages, sentences were pre-processed by a parser and a tree was proposed to the annotators. A rule-based function assigning analytical functions was available as well. Nevertheless, the annotators had to revise the output of both the procedures since the result of these procedures was often inacurate.

After the annotation process had finished, the data passed many checking tests. An example of such a test was verification of the assertion that the verbal nominal predicate (indicated by analytical function Pnom) must always have the verb být (to be) as its head. All the violations of these rules/tests were manually checked and corrected.

The tectogrammatical representation of a sentence captures the following aspects:

The total of 39 attributes is assigned to every non-root node of the tectogrammatical tree; based on the node type (attribute nodetype), only a certain subset of the attributes is necessarily filled in. Often, the attributes are of a list or set type, containing more than one value.

See a sample tree in Figure 2.4, "The tectogrammatical tree of the example sentence (a detailed view)"

As the tectogrammatical structure is also based on the relation of dependency, automated procedures were used to convert dependency-based analytical trees to an intermediate tectogrammatical-like ones. All the generated intermediate trees were then processed by human annotators, who added a great amount of missing information and corrected the wrong one. Coreference, topic-focus articulation, and some of the grammatemes were annotated separately. All the data were then checked by many post-annotation tests (see Section 4.7, "Macros for error detection").

Figure 2.2. Data and annotation workflow diagram

In Figure 2.2, "Data and annotation workflow diagram" the data and work flow diagram is shown. Thick arrows represent iterated operation, double arrows indicate merging procedures that were used when different sub-layers were being annotated on the same data at the same time.