Lexical Semantics and Word Senses › Sense Inventories and Lexical Resources

A sense inventory is the label space for WSD. It lists the senses of each lemma. A resource may also supply definitions, examples, part-of-speech information, and relations to other senses. To specify a correct inventory label, first state the inventory and the labeling protocol. A WSD result is always relative to a particular inventory and version. This relativity is easy to forget. A paper may report "85% accuracy on WSD" without saying which inventory was used. A system may be deployed with a different inventory than the one used in training. A downstream application may expect a coarse distinction while the WSD system produces a fine-grained label. In each case, the inventory is not a detail. It is part of the definition of the task.

Several choices shape a sense inventory:

• Granularity: Should bank have two senses, five senses, or ten? Fine-grained inventories distinguish subtle differences. Coarse-grained inventories merge related uses. Neither is universally correct.
• Part of speech: Are noun, verb, adjective, and adverb senses listed separately? WordNet does this. Check how another inventory groups its parts of speech before mapping its entries.
• Definitions and examples: Are senses defined by glosses, examples, relations, or all three? A short gloss may omit a word used in the target sentence. Adding an example gives a signature the vocabulary of that particular example.
• Relations: Does the inventory encode hypernymy, meronymy, antonymy, entailment, and domain information? Relations support inference and graph-based WSD.
• Identifiers: Are senses identified by names, offsets, keys, or URIs? Record the inventory version and the identifiers used so another reader can resolve the same senses.
• Language and domain: Is the inventory monolingual or multilingual? General-purpose or domain-specific? A biomedical inventory can distinguish senses that a general dictionary ignores.

The lumping versus splitting debate concerns which uses receive separate labels. A splitter inventory gives each nuance its own label. A lumper inventory groups related uses under a broader sense. Finer distinctions can serve applications such as lexicography and translation, but some distinctions are difficult to annotate consistently. Merging difficult distinctions improved agreement in the documented OntoNotes work; a new grouping still needs its own reliability check and may discard distinctions an application needs. Changing the label space changes which predictions count as errors. For the same targets and predictions, mapping both predicted and gold labels through the same deterministic fine-to-coarse map cannot lower raw accuracy: every previously correct label remains correct. Any gain produced by that mapping does not show that the model can recover the merged fine distinctions. Report the inventory when comparing results.

Inter-annotator agreement helps assess how consistently a particular inventory and guideline set can be applied. It is not a universal WSD constant. For example, the SemEval-2007 Task 17 report reports 72% agreement for verbs and 86% for nouns in its fine-grained All-Words annotation using WordNet 2.1 senses. These values belong to that annotation protocol. Disagreement can reflect ambiguous contexts, difficult distinctions, or annotation errors, so it is useful to inspect disputed cases. Agreement between annotators does not impose a mathematical upper bound on a model's accuracy against an adjudicated gold set: a system could match that gold set perfectly even when the annotators initially disagreed.

Princeton WordNet is a lexical database of English. It was developed at Princeton University under George A. Miller in the 1980s, motivated by psycholinguistic theories of lexical memory. The core idea is simple: organize the lexicon by concepts rather than by word forms. Instead of listing words alphabetically and attaching definitions, WordNet groups words into synsets and links those synsets through semantic relations. The result is a graph that reflects conceptual organization. A synset represents a concept. A lemma name may occur in multiple synsets representing distinct readings; those memberships alone do not classify the readings as homonymous or polysemous. A synset may list several lemma names for lexical senses that express the same concept, which is how WordNet represents synonymy.

A WordNet synset is a set of synonymous words that express a distinct concept. Each synset has:

• a unique identifier within a release, such as the displayed synset name bank.n.01
• a part of speech
• a gloss, or short definition
• optional usage examples
• a list of lemma names
• zero or more pointers to related synsets

The pointers encode lexical and semantic relations. For nouns, the most important are hypernymy and hyponymy, which form a hierarchy. For verbs, WordNet includes troponymy, entailment, and cause. For adjectives, the structure is less hierarchical: adjectives are organized into clusters linked by similarity and relatedness. For all parts of speech, WordNet includes antonymy and various domain and usage pointers. The pointers are not decoration. They are the structure that makes WordNet useful for inference. A hypernym link lets a system generalize from a specific concept to a more general one. An antonym link lets a system recognize opposition. A meronym link lets a system relate a part to a whole. These links are imperfect, but they provide a rich source of background knowledge.

The financial sense of bank has a gloss like "a financial institution that accepts deposits and channels the money into lending activities." The riverbank sense has a gloss like "sloping land (especially the slope beside a body of water)." The two glosses explicitly contrast a financial institution with sloping land beside water. They are also the raw material for knowledge-based WSD. A Lesk-style system can compare the words in the sentence with the words in the gloss. A gloss vector system can compare the sentence vector with the gloss vector. A neural system can treat the gloss as a definition and learn to match contexts to definitions. In each case, the gloss is a bridge between the lexical resource and the computational model.

WordNet organizes senses by part of speech and assigns sense numbers. Its ordering uses sparse evidence from semantically tagged text; senses without such evidence can be ordered arbitrarily. The official FAQ cautions against treating that order as a reliable frequency ranking for research. The first listed sense is still a reproducible baseline once the resource version and candidate order are fixed, but it is a proxy for a most-frequent-sense baseline. A true training-corpus MFS baseline counts the senses of each lemma in the specified training data. Neither order nor training frequency guarantees the dominant sense in a new domain.

This notebook uses Princeton WordNet 3.0. Its official statistics list 117,659 synsets, 147,278 distinct strings across parts of speech, and 206,941 word-sense pairs. WordNet 3.1 is a later version, announced on the official web interface in June 2011. The evaluator checks the installed resource version before scoring the fixed WordNet 3.0 gold labels. The version matters because synset identifiers can change, and the same word can have a different number of listed senses. A system evaluated on one version may not reproduce on another. A paper that reports a WordNet-based score should therefore state the version. This is not a minor bibliographic detail. It is part of the experimental setup.

WordNet has limitations that matter for language AI:

• Coverage: Check the fixed release against the vocabulary of the intended domain. A lexical database is not an exhaustive named-entity catalog, and it may lack the sense needed by an application.
• Granularity: Some fine-grained distinctions are difficult to apply consistently and may not match the distinctions needed by a particular application. Different lexicographers might draw the boundaries differently.
• Adjectives: WordNet uses antonymy/similarity clusters. Check the available relation types rather than applying noun-style hypernym navigation to every adjective entry.
• Frequency estimates: The standard WordNet 3.0 counts pool tagged semantic concordances. These pooled counts are not a measurement of a new target corpus. Measure sense frequencies in the domain, genre, and period relevant to the application.
• Language scope: Princeton WordNet is English. The Open Multilingual WordNet and BabelNet document their own language coverage; check that coverage for the application.

Despite these limitations, WordNet remains a standard reference point. The NLTK interface used below lets us inspect its sense records and relations. It is also a useful teaching tool because its structure is transparent. You can inspect a synset, read its gloss, follow its hypernym links, and see how the lexicon is organized. This transparency makes WordNet a good starting point for understanding lexical semantics, even when a modern application uses a different resource.

FrameNet is based on frame semantics, which describes word meanings against background situations. A frame is a schematic situation with participants and props, called frame elements. A lexical unit pairs a word or phrase in one of its senses with a frame. Different lexical units can present a related situation from different perspectives, foregrounding different participants.

The Commerce_buy frame, for example, involves a Buyer, a Seller, Goods, and Money. The verbs buy and purchase present the transaction from the buyer's perspective; words such as sell and pay express related commerce perspectives. Lexical units evoke frames, while their arguments fill frame elements. FrameNet describes how these words combine with their arguments and what roles those arguments play. A sentence like "She bought a bicycle from him for two hundred dollars" evokes the frame with the Buyer as subject, the Goods as object, the Seller as source, and the Money as price. The frame provides a structured interpretation that goes beyond the dictionary sense of buy. It tells us which participants are expected and how they are typically expressed.

FrameNet organizes sense-specific lexical units through frames and records the syntactic patterns that express frame elements. Its coverage should be checked for the lexical units an application needs. The bicycle example separates frame selection from identifying the argument fillers. We will return to these ideas in the chapter on semantic roles and argument structure. For now, the important point is that lexical meaning is not only a matter of synonymy and hyponymy. It is also a matter of the frames and roles that words bring into a sentence. A WSD system that only predicts a synset may miss the relational structure that FrameNet captures. A frame-based system may capture that structure without assigning a WordNet sense. The two resources answer different questions, and they are useful in different ways.

OntoNotes is a large multilingual corpus with annotations for syntax, predicates, coreference, and word senses. The documented English sense inventory is coarser than WordNet's. The OntoNotes project grouped English WordNet senses into broader sense groups, reducing the number of distinctions and improving inter-annotator agreement. The coarsening is not a retreat from semantic precision. It is a practical response to the difficulty of annotating fine-grained distinctions consistently. Unresolved disagreement over a fine distinction can leave the supervisory label uncertain. A coarser inventory may lose some information, but it can produce cleaner supervision and more reliable evaluation.

OntoNotes should not be confused with the inventories used in later multilingual SemEval tasks. SemEval-2013 Task 12 used BabelNet 1.1.1 and allowed WordNet 3.0 and Wikipedia projections; SemEval-2015 Task 13 used BabelNet 2.5.1. These are not OntoNotes evaluations. More generally, a higher score on a coarser label space does not by itself establish better fine-grained discrimination: merging labels changes which predictions count as errors.

For English OntoNotes, the documented grouping procedure merges several fine WordNet senses into broader groups. Such coarsening loses information: a prediction of the group alone cannot identify which member fine sense was intended. The grouping description does not establish a reverse split of a single WordNet sense into several groups for the same lemma. A comparison across resources must state the mapping and its direction; mapping a fine label to a group and recovering a fine label from that group are different tasks.

BabelNet is a large multilingual encyclopedic dictionary and semantic network. It combines WordNet, Wikipedia, Wiktionary, and other resources into multilingual synsets called BabelSynsets. A BabelSynset groups the available lexicalizations for a concept or named entity. Those lexicalizations can span languages. BabelNet is useful for multilingual WSD, cross-lingual transfer, and entity linking, but its automatically constructed lexicalizations can be imperfect. The automatic integration of resources makes BabelNet broad, but it also means that the quality of individual entries can vary. BabelSynsets can group cross-language word senses that are semantically equivalent or nearly equivalent. When using its translations, select the sense that fits the occurrence. BabelNet is therefore a powerful resource that requires careful validation for high-stakes applications.

Wiktionary is a crowdsourced dictionary with broad coverage of languages, dialects, slang, and technical terms. Its entries can include examples and translations. Compare dated snapshots when assessing coverage or currency; collaborative editing alone does not establish that every entry is newer or better than a fixed WordNet release. Wiktionary specifies a standard entry layout and permits discussed variations. For low-resource languages and neologisms, Wiktionary can fill gaps that WordNet leaves open. It is also a useful source of usage examples and translations. An extraction pipeline still needs to account for allowed structural variation and language-specific conventions. Formatting can be handled during extraction or by an upstream extractor. If an application interprets definitions, check how it treats full definitions, translation glosses, and non-gloss definitions. For sense prediction or translations aligned to individual senses, state which source distinctions are preserved and how differing granularities are mapped. The crowdsourced nature of Wiktionary is both its strength and its weakness: it captures living language, but it does not guarantee uniform quality.

Domain-specific resources organize terminology for particular fields. UMLS and SNOMED CT cover biomedical concepts; MeSH supports biomedical literature indexing. Their concept identifiers and subject headings serve different purposes from a general-purpose word-sense label. For a domain application, check which distinctions and output identifiers the task requires. If the selected inventory already represents those distinctions, changing the model or its training data may be sufficient.

A recurring practical problem is that different resources carve meaning differently. WordNet may distinguish ten senses of a word, OntoNotes may distinguish three, and a domain ontology may distinguish five completely different ones. A WSD system trained on one inventory cannot be evaluated directly on another without a mapping, and the mapping need not be one-to-one. The mismatch is not only a matter of counting senses. It is a matter of where the boundaries are drawn and which distinctions are treated as central. Two inventories may agree that a word has multiple meanings, but disagree about which meanings should be grouped together. Two inventories may use the same label for different concepts, or different labels for the same concept. The result is a translation problem between label spaces.

This has consequences for reproducibility and for claims about state-of-the-art performance. A score on SemEval-2015 is not comparable to a score on a WordNet-based lexical sample task unless the inventories and evaluation conditions match. When you read a WSD result, always ask:

• Which sense inventory was used?
• Which version of the inventory?
• Was the task lexical sample or all-words?
• Which parts of speech were evaluated?
• What was the most-frequent-sense baseline?
• How were multiword expressions and named entities handled?

These questions are not bureaucratic. They determine what the number means. A score without an inventory is like a distance without a unit. It may be numerically precise, but it is semantically incomplete. A careful evaluation reports the inventory, the version, the task variant, the baseline, and the handling of edge cases. A careful reader asks for those details before comparing systems.