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ABSTRACT
Semantic knowledge (or semantic memory) is knowledge we have about the world. For example, we know that knives are typically sharp, made of metal, and that they are tools used for cutting. To what kinds of experiences do we owe such knowledge? Most work has stressed the role of direct sensory and motor experiences. Another kind of experience, considerably less well understood, is our experience with language. We review two ways of thinking about the relationship between language and semantic knowledge: (i) language as mapping onto independently-acquired concepts, and (ii) language as a set of cues to meaning. We highlight some problems with the words-as-mappings view, and argue in favour of the words-as-cues alternative. We then review some surprising ways that language impacts semantic knowledge, and discuss how distributional semantics models can help us better understand its role. We argue that language has an abstracting effect on knowledge, helping to go beyond concrete experiences which are more characteristic of perception and action. We conclude by describing several promising directions for future research.
Acknowledgements
We thank Bill Thompson for critical help with the cross-linguistic analyses reported here.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 According to the Language of Thought hypothesis (Fodor, Citation1975, Citation2010), although many semantic facts are learned (for example that switchblade knives are illegal in some places), the (lexical) concept KNIFE – and all other lexical concepts – are innate. This assertion is largely ignored by practicing cognitive scientists and cognitive neuroscientists, but generative linguistics (or at least the minimalist programme version of it) seems to depend on the a priori existence of lexical concepts because otherwise the merge operation would have nothing to merge (Chomsky, Citation2010; see Bickerton, Citation2014 for discussion).
2 Language use of course involves perception and action, but for our purposes it is useful to distinguish between perceiving nonverbal stimuli (e.g. a real dog or a picture of a dog) and perceiving linguistic/symbolic stimuli (the word “dog”), and, likewise, distinguishing actions involved in making a sandwich and the action of using language to describe making a sandwich.
3 A somewhat different sense of “the mapping problem” involves figuring out the local reference of a word, i.e. that an utterance of “apple” refers to the particular apple sitting on the table (Lewis & Frank, Citation2013; McMurray, Horst, & Samuelson, Citation2012).
4 Not everyone who relies on the words-as-mapping view denies the role of words on conceptual development. For example, Waxman and colleagues have long argued that words facilitate infants’ and older children's category learning (Balaban & Waxman, Citation1997; Fulkerson & Waxman, Citation2007; Waxman & Markow, Citation1995). Findings that words facilitate category learning are unexpected on the view that the categories existed prior to word learning.
5 One especially well-studied case of ambiguity is the word “some” which can either mean “some but not all” or “all”. One proposed solution to such ambiguities is to posit that there is a core meaning of “some” which is then modified by pragmatics making the word only look polysemous (Grice, Citation1957; Huang & Snedeker, Citation2009; see also Jackendoff, Citation1990). While the role of pragmatics in constructing meaning is indisputable, we are unsure of what independent evidence supports the existence of “core meanings” in the minds of the speakers – meanings that are simultaneously abstract and precise enough to give rise to all the attested senses.
6 This phenomenon was termed “instantiation”: a process wherein a more general (e.g. “fish”) is interpreted as a more specific word (e.g. “shark”). Debate ensued as to whether instantiation effects were better understood as “refocusing” or “restructuring” (see Roth & Shoben, Citation1983 for discussion). For present purposes, we ignore the difference between these accounts, but note the parallel between such instantiation effects and those described by Zwaan, Stanfield, and Yaxley (Citation2002) wherein people e.g. recognise a picture of an eagle with an outstretched wings faster after reading a sentence about an eagle in the sky.
7 We are not claiming that there are no universal dimensions to linguistically expressed meanings and are sympathetic to proposals such as Wierzbicka and Goddard's Natural Semantic Metalanguage (Goddard & Wierzbicka, Citation2002; Wierzbicka, Citation1996), but the semantic primes of this proposed metalanguage share little with the kinds of concepts (CHAIR, DOG, SCHOOL) to which words are often thought to map onto.
8 Russian speakers refer to the hand by using the phrase “kist’ ruki”, but this phrase refers to a part of the arm rather than to a separate body part. To the extent that English speakers endorse the claim that the hand is attached to the arm rather than being a part of the arm, the meanings of “kist’ ruki” is not a direct translation of “hand”. Of potential interest, the typical meaning of kist’ is a [paint]brush, and is historically derived from the root that denoted a “bunch” or “bundle” (e.g. of twigs). The additional sense to refer to the hand is a later derivation (Fasmer, Citation2009). To speculate, there may be an analogy drawn between the fingers of the hand and the bristles of a brush, though such connections are unlikely to be psychologically real. For additional discussion of the semantic organisation of knowledge related to the body, see Majid (Citation2015).
9 See http://iplayif.com/?story=http://parchment.toolness.com/if-archive/games/zcode/gostak.z5.js to get a first-hand sense of semantics conveyed purely by English syntax and morphology.
10 See also https://research.googleblog.com/2017/03/an-upgrade-to-syntaxnet-new-models-and.html for an example of the latest version of Google's state-of-the-art parser (Parsey McParseface) applied to such “meaningless” sentences.
11 The example comes from an online lecture by Baroni http://docplayer.net/31565915-Distributional-semantics.html.
12 We focus on this skip-gram instantiation of the word2vec model. An alternative instantiation is continuous bag of words (CBOW) which involves presenting the context as input and learning to predict the target word. Besides neural network based models which are trained gradually using sliding word or context windows, there are now large-scale models utilising global word co-occurrence counts. The most successful of these is GLoVE (Pennington, Socher, & Manning, Citation2014) which performs a bit better than word2vec when trained on very large corpora and somewhat worse when trained on smaller corpora.
15 The networks’ performance can also be fragile. For example, the shark/goldfish analogies ((E–F)) work for a model trained on Wikipedia, but not for one trained on the Google News corpus. The Wikipedia-trained model correctly relates cats:leopards to dogs:wolves, but fails to relate cats:lions to dogs:wolves, instead outputting bulls, eagles, donkeys in place of “wolves”. It also fails relating cat:leopard to dog:wolf, outputting in place of “dog” lion, boar, and leopard. Note, however, that the model still shows sensitivity to the count status of the nouns.
16 The idea of motivation is related to Grice's distinction between natural and non-natural meaning (Grice, Citation1957), with natural meaning mapping being motivated and non-natural (i.e. conventional) being unmotivated. There are some differences though. For example, using applause to signal approval might be seen as non-natural in that it is conventional and non-indexical, but it is nevertheless motivated in that the length of applause tends to correlate with the amount of approval. One cannot applaud without committing to some length and the length conveys meaning.