Lexical Semantics and Word Senses › Worked Example: Disambiguating Bank

Let's work through a small example by hand before implementing anything. Consider the sentence:

The fisherman sat on the bank of the river and watched the current.

We remove stopwords and stem the remaining content words. The context contains something like:

fisherman, sat, bank, river, watch, current

We remove the stem of the target bank from the context set. Now we compare the context with the glosses and examples of the candidate senses. A target self-match can be weak evidence when the target happens to occur in a signature. WordNet definitions and examples are shared among synset synonyms, so not every signature contains the queried word. The goal is to measure the evidence from the surrounding words, not from the target itself.

For the riverbank sense, WordNet's gloss is "sloping land (especially the slope beside a body of water)." The examples include "they pulled the canoe up on the bank" and "he sat on the bank of the river and watched the currents". The overlap with our context includes sat, river, watch, and current. The score is 4. The overlap is meaningful because these words describe the scene. The unchanged Porter stem sat matches the action in the sentence. River matches the location. Watch matches the action. Current matches the object being watched.

For the financial sense, the gloss is "a financial institution that accepts deposits and channels the money into lending activities." The examples include "he cashed a check at the bank" and "that bank holds the mortgage on my home". The overlap with our context is empty. The score is 0. The financial gloss contains words like financial, institution, deposits, money, lending, but none of these words appear in the fisherman sentence. This signature contributes no positive overlap evidence for the financial reading.

For the flight maneuver sense, the gloss mentions aircraft and lateral tipping. The overlap is empty. The score is 0. The sentence has no aviation context. This signature contributes no positive overlap evidence for the flight reading.

The riverbank sense wins clearly. Our sentence closely follows the second WordNet example, which supplies all four matching stems. Use independently sampled contexts to test whether that benefit carries over. The gloss alone might not contain sat, watch, or current. The examples provide additional vocabulary that matches the sentence. Related-sense glosses can extend the signature further.

Now consider the financial sentence:

She deposited her paycheck at the bank before noon.

After removing bank, the context contains the stems deposit, paycheck, befor, and noon. The financial gloss contains deposits, which has the same stem as deposited. The score is 1. The riverbank signature shares none of these stems. Among these three selected noun candidates, the financial sense has the largest overlap. The example shows how stemming helps. If you compare the words as written, deposited and deposits do not match. The score might be 0, and the tie-break might choose the wrong sense. Stemming is a crude form of morphological normalization; here it connects the inflected forms in the sentence and gloss.

The lexical evidence remains limited. Changing paycheck to salary alone leaves the shared deposit stem intact. In the shorter sentence "She walked toward the bank," approaching a financial institution and approaching a river edge are both compatible with the words supplied. A later mention of a teller or of the water can clarify the intended reading. An overlap method may still lack a matching cue in the available window, so inspect its actual shared words and candidate scores.

The lesson is that lexical overlap is a useful signal, but it is not a semantic model. It captures surface similarity between the context words and the signature. Systems intended to handle a wide range of WSD cases need to handle morphology, synonymy, syntax, and world knowledge. The overlap demonstration tests one source of evidence; those broader capabilities need their own evaluation. A knowledge-based method like Lesk is a good starting point because it makes the evidence visible. It shows which words support which sense. But it is not a complete solution. A supervised or contextual model can learn patterns that are not present in the gloss. A hybrid system can combine the interpretability of Lesk with the flexibility of neural representations.