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In the word2vec model, each word is represented by a vector, you can then measure the semantic similarity between two words by measuring the cosine of the vectors representing th words. Semantic similar words should have a high cosine similarity, for instance:
When Latent Semantic Analysis refers to a "document", it basically means any set of words that is longer than 1. You can use it to compute the similarity between a document and another document, between a word and another word, or between a word and a document. So you could certainly use it for your chosen application.
Still in the NLTK, check out the discussion of the method similar() in the introduction to the NLTK book, and the class nltk.text.ContextIndex that it's based on. (All pretty simple still, but it might be all you really need).
Ads may show on searches that include the meaning of your keyword. The meaning of the keyword can be implied, and user searches can be a more specific form of the meaning. With phrase match, you can reach more searches than with exact match and fewer searches than with broad match, only showing your ads on the searches that include your product or service.
Ads may show on searches that have the same meaning or same intent as the keyword. Of the 3 keyword matching options, exact match gives you the most steering over who views your ad, but reaches fewer searches than both phrase and broad match.
A companion is a bill introduced in one house that is identical or similar to a bill introduced in the other house. Use of companion bills permits their concurrent analysis and deliberation by both houses. Companion bills which are identical word-for-word, not including titles, are marked "identical" in bill history. However, Resolutions and Concurrent Resolutions are considered identical when the only difference is the word "House" or "Senate." Companion bills are marked "similar" in bill history if they are substantially similar in text or have substantial portions of text that are identical or largely the same. If one word is different, the bills are "similar." Companion bills with selected provisions that are similar in text are marked "compare" in bill history.
Every line from August hits too close to home. I was wondering if anyone knew any similar themed songs? I'm not so particular about songs that are similar sonically, but more of songs that touch on similar themes of nostalgia and unrequited love. Recommendations to other Taylor Swift songs are also welcome ?
In historical linguistics, cognates or lexical cognates are sets of words that have been inherited in direct descent from an etymological ancestor in a common parent language.[1] Because language change can have radical effects on both the sound and the meaning of a word, cognates may not be obvious, and often it takes rigorous study of historical sources and the application of the comparative method to establish whether lexemes are cognate. Cognates are distinguished from loanwords, where a word has been borrowed from another language.
Cognates need not have the same meaning, as they may have undergone semantic change as the languages developed independently. For example English starve and Dutch sterven 'to die' or German sterben 'to die' all descend from the same Proto-Germanic verb, *sterbaną 'to die'.
Likewise, English much and Spanish mucho look similar and have a similar meaning, but are not cognates: much is from Proto-Germanic *mikilaz < PIE *meǵ- and mucho is from Latin multum < PIE *mel-. A true cognate of much is the archaic Spanish maño 'big'.[4]
An etymon, or ancestor word, is the ultimate source word from which one or more cognates derive.In other words, it is the source of related words in different languages. For example, the etymon of both Welsh ceffyl and Irish capall is the Proto-Celtic *kaballos (all meaning horse).
False cognates are pairs of words that seem to be cognates because of similar sounds and meaning, but have different etymologies; they can be within the same language or from different languages, even within the same family.[1] For example, the English word dog and the Mbabaram word dog have exactly the same meaning and very similar pronunciations, but by complete coincidence. Likewise, English much and Spanish mucho came by their similar meanings via completely different Proto-Indo-European roots, and same for English have and Spanish haber. This is different from false friends, which are similar-sounding words with different meanings, and may or may not be cognates.
The term "false cognate" is sometimes misused to refer to false friends, but the two phenomena are distinct.[1][2] False friends occur when two words in different languages or dialects look similar, but have different meanings. While some false friends are also false cognates, many are genuine cognates (see False friends Causes).[2] For example, English pretend and French prétendre are false friends, but not false cognates, as they have the same origin.[3]
Similarly, the Hebrew word דיבוב dibúv ("speech, inducing someone to speak"), which is a false cognate of (and thus etymologically unrelated to) the phono-semantically similar English word dubbing, is then used in the Israeli phono-semantic matching for dubbing. The result is that in today's Israel, דיבוב dibúv means "dubbing".[19]
We classify the findings into clusters of similar independent climate variables and assess the explanatory power of the climate indicators included in each cluster. The studies contain a variety of climate-related variables, further referred to as climate variables. A study can include and assess multiple climate variables and therefore be included in multiple clusters. Based on our sample, we distinguish five clusters of climate variables: (climate-related) natural disasters, basic climate variability (BCV), advanced climate variability (ACV), freshwater availability, and El Niño/Southern Oscillation (ENSO). We categorize each climate operationalization into a single cluster; however, papers using multiple operationalizations may show up in more clusters. These clusters are named after their key inclusion criteria.
The primary indices applied in these studies are the Standardized Precipitation Index (SPI) and Temperature Index (TI). Couttenier and Soubeyran (2014) apply the Palmer Drought Severity Index (PDSI). von Uexkull et al. (2016) use the SPEI which includes potential evapotranspiration. Jones et al. (2017) find a direct link between the SPI-1 and violent events whereas Raleigh and Kniveton (2012) find direct links between both positive and negative deviations from the SPI-1 (i.e., situations with wetter and dryer conditions than long-term average) and rebel events and non-state conflicts in East Africa. However, Landis (2014) did not find a similar correlation at the global level while Nordkvelle et al. (2017) did not find evidence for a link with positive nor negative binary SPI-1 deviations and non-state conflicts.
Case studies and regional analysis revealed the complexity of such causal mechanisms and how climate, and the chosen climate operationalization, acts on the different components. See for example Ide et al. (2020) on small-scale (water-related) conflict during drought in the MENA region, Abrahams (2020) on climate-conflict interventions in Karamoja region, Uganda, and van Weezel (2019) on precipitation decline and communal conflict in Ethiopia and Kenya. The assessment whether a more comprehensive understanding of local conditions and conflict or peacebuilding pathways could lead to alternative operationalization methods than those presented in this study could provide valuable insights. For example, to inform the development of climate indicators for large-N studies. We argue that if it is, for example water shortage and failed harvest, that people respond to, future studies should consider the combined impact of climate variability, water use, and water regulation infrastructure. At least, these studies should control for the water use and water regulation factors in similar ways as other intervening factors are controlled for. Alternatively, studies could make use of model-based variables who integrate these various components. Global water balance models that integrate climate, water use, and infrastructure, such as the PCR-GLOBWB framework (Sutanudjaja et al. 2018), could be a useful source to create these model-derived variables. Hoch et al. (2021) demonstrated this by applying a model-derived independent variable for the upper soil water storage. Although a conflict may affect local (data on) water use and infrastructure, the use of a model-based approach for generating the indicator and resulting time series ensures no interference from the actors involved in the conflict.
The empirical evidence at the global, country-year level regarding the impact of climate-related natural disasters on the risk of civil war onset is limited. However, the presence of pre-existing conditions plays a crucial role when examining these climate disaster-conflict relations. We demonstrated a similar lack of evidence for the relation between civil war onset and El Niño years.
A claim that a food is low in energy, and any claim likely to have the same meaning for the consumer, may only be made where the product does not contain more than 40 kcal (170 kJ)/100 g for solids or more than 20 kcal (80 kJ)/100 ml for liquids.
A claim that a food is energy-reduced, and any claim likely to have the same meaning for the consumer, may only be made where the energy value is reduced by at least 30%, with an indication of the characteristic(s) which make(s) the food reduced in its total energy value.
A claim that a food is low in fat, and any claim likely to have the same meaning for the consumer, may only be made where the product contains no more than 3 g of fat per 100 g for solids or 1,5 g of fat per 100 ml for liquids (1,8 g of fat per 100 ml for semi-skimmed milk).
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